Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method includes: performing motion compensation on a plurality of encoded point clouds; merging the plurality of encoded point clouds that have been motion compensated to generate a reference point cloud; generating an N-ary tree structure of a current point cloud, where N is an integer greater than or equal to 2; and encoding the N-ary tree structure of the current point cloud using the reference point cloud.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2021/033888 filed on Sep. 15, 2021,claiming the benefit of priority of U.S. Provisional Patent ApplicationNo. 63/080,273 filed on Sep. 18, 2020, the entire contents of which arehereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point cloud in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point cloud are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point cloudnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include Moving Picture ExpertsGroup-4 Advanced Video Coding (MPEG-4 AVC) and High Efficiency VideoCoding (HEVC) standardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle by using three-dimensionalmap data is known (see, for example, Patent Literature (PTL) 1(International Publication WO 2014/020663)).

SUMMARY

There has been a demand for improving coding efficiency in athree-dimensional data encoding process and a three-dimensional datadecoding process.

The present disclosure has an object to provide a three-dimensional dataencoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of improving coding efficiency.

A three-dimensional data encoding method according to an aspect of thepresent disclosure includes: performing motion compensation on aplurality of encoded point clouds; merging the plurality of encodedpoint clouds that have been motion compensated to generate a referencepoint cloud; generating an N-ary tree structure of a current pointcloud, where N is an integer greater than or equal to 2; and encodingthe N-ary tree structure of the current point cloud using the referencepoint cloud.

A three-dimensional data decoding method according to an aspect of thepresent disclosure includes: performing motion compensation on aplurality of decoded point clouds; merging the plurality of decodedpoint clouds that have been motion compensated, to generate a referencepoint cloud; decoding an N-ary tree structure of a current point cloudusing the reference point cloud, where N is an integer greater than orequal to 2; and generating a decoded point cloud of the current pointcloud from the N-ary tree structure of the current point cloud.

The present disclosure provides a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of improving coding efficiency.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram illustrating a configuration of a three-dimensionaldata encoding and decoding system according to Embodiment 1;

FIG. 2 is a diagram illustrating a structure example of point cloud dataaccording to Embodiment 1;

FIG. 3 is a diagram illustrating a structure example of a data fileindicating the point cloud data according to Embodiment 1;

FIG. 4 is a diagram illustrating types of the point cloud data accordingto Embodiment 1;

FIG. 5 is a diagram illustrating a structure of a first encoderaccording to Embodiment 1;

FIG. 6 is a block diagram illustrating the first encoder according toEmbodiment 1;

FIG. 7 is a diagram illustrating a structure of a first decoderaccording to Embodiment 1;

FIG. 8 is a block diagram illustrating the first decoder according toEmbodiment 1;

FIG. 9 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 1;

FIG. 10 is a diagram showing an example of geometry informationaccording to Embodiment 1;

FIG. 11 is a diagram showing an example of an octree representation ofgeometry information according to Embodiment 1;

FIG. 12 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 1;

FIG. 13 is a block diagram of an attribute information encoder accordingto Embodiment 1;

FIG. 14 is a block diagram of an attribute information decoder accordingto Embodiment 1;

FIG. 15 is a block diagram showing a configuration of the attributeinformation encoder according to the variation of Embodiment 1;

FIG. 16 is a block diagram of the attribute information encoderaccording to Embodiment 1;

FIG. 17 is a block diagram showing a configuration of the attributeinformation decoder according to the variation of Embodiment 1;

FIG. 18 is a block diagram of the attribute information decoderaccording to Embodiment 1;

FIG. 19 is a diagram illustrating a structure of a second encoderaccording to Embodiment 1;

FIG. 20 is a block diagram illustrating the second encoder according toEmbodiment 1;

FIG. 21 is a diagram illustrating a structure of a second decoderaccording to Embodiment 1;

FIG. 22 is a block diagram illustrating the second decoder according toEmbodiment 1;

FIG. 23 is a diagram illustrating a protocol stack related to PCCencoded data according to Embodiment 1;

FIG. 24 is a diagram illustrating structures of an encoder and amultiplexer according to Embodiment 2;

FIG. 25 is a diagram illustrating a structure example of encoded dataaccording to Embodiment 2;

FIG. 26 is a diagram illustrating a structure example of encoded dataand a NAL unit according to Embodiment 2;

FIG. 27 is a diagram illustrating a semantics example ofpcc_nal_unit_type according to Embodiment 2;

FIG. 28 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 3;

FIG. 29 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 3;

FIG. 30 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 3;

FIG. 31 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 3;

FIG. 32 is a diagram illustrating an example of inter predictionaccording to Embodiment 3;

FIG. 33 is a diagram illustrating a syntax example of an SPS accordingto Embodiment 3;

FIG. 34 is a diagram illustrating a syntax example of a GPS according toEmbodiment 3;

FIG. 35 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 3;

FIG. 36 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 3;

FIG. 37 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 4;

FIG. 38 is a flowchart of a three-dimensional data creation methodaccording to Embodiment 4;

FIG. 39 is a diagram showing a structure of a system according toEmbodiment 4;

FIG. 40 is a block diagram of a client device according to Embodiment 4;

FIG. 41 is a block diagram of a server according to Embodiment 4;

FIG. 42 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 4;

FIG. 43 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 4;

FIG. 44 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 4;

FIG. 45 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 4;

FIG. 46 is a diagram illustrating a structure of a variation of thesystem according to Embodiment 4;

FIG. 47 is a diagram illustrating a structure of the server and clientdevices according to Embodiment 4;

FIG. 48 is a diagram illustrating a configuration of the server andclient devices according to Embodiment 4;

FIG. 49 is a flowchart of a process performed by the client deviceaccording to Embodiment 4;

FIG. 50 is a diagram illustrating a configuration of a sensorinformation collection system according to Embodiment 4;

FIG. 51 is a diagram illustrating an example of a system according toEmbodiment 4;

FIG. 52 is a diagram illustrating a variation of the system according toEmbodiment 4;

FIG. 53 is a flowchart illustrating an example of an application processaccording to Embodiment 4;

FIG. 54 is a diagram illustrating the sensor range of various sensorsaccording to Embodiment 4;

FIG. 55 is a diagram illustrating a configuration example of anautomated driving system according to Embodiment 4;

FIG. 56 is a diagram illustrating a configuration example of a bitstreamaccording to Embodiment 4;

FIG. 57 is a flowchart of a point cloud selection process according toEmbodiment 4;

FIG. 58 is a diagram illustrating a screen example for point cloudselection process according to Embodiment 4;

FIG. 59 is a diagram illustrating a screen example of the point cloudselection process according to Embodiment 4; and

FIG. 60 is a diagram illustrating a screen example of the point cloudselection process according to Embodiment 4.

DETAILED DESCRIPTION OF THE EMBODIMENTS

A three-dimensional data encoding method according to an aspect of thepresent disclosure includes: performing motion compensation on aplurality of encoded point clouds; merging the plurality of encodedpoint clouds that have been motion compensated to generate a referencepoint cloud; generating an N-ary tree structure of a current pointcloud, where N is an integer greater than or equal to 2; and encodingthe N-ary tree structure of the current point cloud using the referencepoint cloud.

Accordingly, in the three-dimensional data encoding method, encodingefficiency can be improved by encoding a current point cloud using areference point cloud obtained by merging a plurality of encoded pointclouds.

For example, the encoding of the N-ary tree structure of the currentpoint cloud may include: performing motion compensation for the currentpoint cloud on the reference point cloud; generating an N-ary treestructure of the reference point cloud that has been motion compensated;and encoding the N-ary tree structure of the current point cloud usingthe N-ary tree structure of the reference point cloud.

For example, the encoding of the N-ary tree structure of the currentpoint cloud may include: entropy encoding the N-ary tree structure ofthe current point cloud; and controlling a probability parameter to beused in the entropy encoding, based on the reference point cloud.

For example, the three-dimensional data encoding method may furtherinclude: generating an encoded current point cloud from the N-ary treestructure of the current point cloud; performing motion compensation forthe reference point cloud on the encoded current point cloud; andmerging the encoded current point cloud that has been motion compensatedwith the reference point cloud to update the reference point cloud.

For example, each of the plurality of encoded point clouds may belong toa different frame than the current point cloud.

For example, each of the plurality of encoded point clouds may belong toa same frame as the current point cloud.

For example, the three-dimensional data encoding method may furtherinclude: storing, in control information which is common to a pluralityof point clouds, first information indicating whether execution ofencoding using the reference point cloud is permitted.

For example, the three-dimensional data encoding method may furtherinclude: storing, in the control information, second information on atotal number of the plurality of encoded point clouds, when the firstinformation indicates that the execution of the encoding using thereference point cloud is permitted.

A three-dimensional data decoding method according to an aspect of thepresent disclosure includes: performing motion compensation on aplurality of decoded point clouds; merging the plurality of decodedpoint clouds that have been motion compensated, to generate a referencepoint cloud; decoding an N-ary tree structure of a current point cloudusing the reference point cloud, where N is an integer greater than orequal to 2; and generating a decoded point cloud of the current pointcloud from the N-ary tree structure of the current point cloud.

Accordingly, in the three-dimensional data decoding method, the currentpoint cloud can be decoded using a reference point cloud obtained bymerging a plurality of decoded point clouds.

For example, the decoding of the N-ary tree structure of the currentpoint cloud may further include: performing motion compensation for thecurrent point cloud on the reference point cloud; generating an N-arytree structure of the reference point cloud that has been motioncompensated; and decoding the N-ary tree structure of the current pointcloud using the N-ary tree structure of the reference point cloud.

For example, the decoding of the N-ary tree structure of the currentpoint cloud may include: entropy decoding the N-ary tree structure ofthe current point cloud; and controlling a probability parameter to beused in the entropy decoding, based on the reference point cloud.

For example, the three-dimensional data decoding method may furtherinclude: performing motion compensation for the reference point cloud onthe decoded point cloud of the current point cloud; and merging thedecoded point cloud that has been motion compensated with the referencepoint cloud to update the reference point cloud.

For example, each of the plurality of decoded point clouds may belong toa different frame than the current point cloud.

For example, each of the plurality of decoded point clouds may belong toa same frame as the current point cloud.

For example, the three-dimensional data decoding method may furtherinclude: obtaining, from control information which is common to aplurality of point clouds, first information indicating whetherexecution of decoding using the reference point cloud is permitted.

For example, the three-dimensional data decoding method may furtherinclude: obtaining, from the control information, second information ona total number of the plurality of decoded point clouds, when the firstinformation indicates that the execution of the decoding using thereference point cloud is permitted.

A three-dimensional data encoding device according to an aspect of thepresent disclosure includes: a processor; and memory. Using the memory,the processor: performs motion compensation on a plurality of encodedpoint clouds; merges the plurality of encoded point clouds that havebeen motion compensated to generate a reference point cloud; generatesan N-ary tree structure of a current point cloud, where N is an integergreater than or equal to 2; and encodes the N-ary tree structure of thecurrent point cloud using the reference point cloud.

Accordingly, the three-dimensional data encoding device can improveencoding efficiency by encoding a current point cloud using a referencepoint cloud obtained by merging a plurality of encoded point clouds.

Furthermore, a three-dimensional data decoding device according to anaspect of the present disclosure includes: a processor; and memory.Using the memory, the processor: performs motion compensation on aplurality of decoded point clouds; merges the plurality of decoded pointclouds that have been motion compensated, to generate a reference pointcloud; decodes an N-ary tree structure of a current point cloud usingthe reference point cloud, where N is an integer greater than or equalto 2; and generates a decoded point cloud of the current point cloudfrom the N-ary tree structure of the current point cloud.

Accordingly, the three-dimensional data decoding device can decode thecurrent point cloud using a reference point cloud obtained by merging aplurality of decoded point clouds.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a CD-ROM, ormay be implemented as any combination of a system, a method, anintegrated circuit, a computer program, and a recording medium.

Hereinafter, embodiments will be specifically described with referenceto the drawings. It is to be noted that each of the followingembodiments indicate a specific example of the present disclosure. Thenumerical values, shapes, materials, constituent elements, thearrangement and connection of the constituent elements, steps, theprocessing order of the steps, etc., indicated in the followingembodiments are mere examples, and thus are not intended to limit thepresent disclosure. Among the constituent elements described in thefollowing embodiments, constituent elements not recited in any one ofthe independent claims will be described as optional constituentelements.

Embodiment 1

When using encoded data of a point cloud in a device or for a service inpractice, required information for the application is desirablytransmitted and received in order to reduce the network bandwidth.However, conventional encoding structures for three-dimensional datahave no such a function, and there is also no encoding method for such afunction.

Embodiment 1 described below relates to a three-dimensional dataencoding method and a three-dimensional data encoding device for encodeddata of a three-dimensional point cloud that provides a function oftransmitting and receiving required information for an application, athree-dimensional data decoding method and a three-dimensional datadecoding device for decoding the encoded data, a three-dimensional datamultiplexing method for multiplexing the encoded data, and athree-dimensional data transmission method for transmitting the encodeddata.

In particular, at present, a first encoding method and a second encodingmethod are under investigation as encoding methods (encoding schemes)for point cloud data. However, there is no method defined for storingthe configuration of encoded data and the encoded data in a systemformat. Thus, there is a problem that an encoder cannot perform an MUXprocess (multiplexing), transmission, or accumulation of data.

In addition, there is no method for supporting a format that involvestwo codecs, the first encoding method and the second encoding method,such as point cloud compression (PCC).

With regard to this embodiment, a configuration of PCC-encoded data thatinvolves two codecs, a first encoding method and a second encodingmethod, and a method of storing the encoded data in a system format willbe described.

A configuration of a three-dimensional data (point cloud data) encodingand decoding system according to this embodiment will be firstdescribed. FIG. 1 is a diagram showing an example of a configuration ofthe three-dimensional data encoding and decoding system according tothis embodiment. As shown in FIG. 1 , the three-dimensional dataencoding and decoding system includes three-dimensional data encodingsystem 4601, three-dimensional data decoding system 4602, sensorterminal 4603, and external connector 4604.

Three-dimensional data encoding system 4601 generates encoded data ormultiplexed data by encoding point cloud data, which isthree-dimensional data. Three-dimensional data encoding system 4601 maybe a three-dimensional data encoding device implemented by a singledevice or a system implemented by a plurality of devices. Thethree-dimensional data encoding device may include a part of a pluralityof processors included in three-dimensional data encoding system 4601.

Three-dimensional data encoding system 4601 includes point cloud datageneration system 4611, presenter 4612, encoder 4613, multiplexer 4614,input/output unit 4615, and controller 4616. Point cloud data generationsystem 4611 includes sensor information obtainer 4617, and point clouddata generator 4618.

Sensor information obtainer 4617 obtains sensor information from sensorterminal 4603, and outputs the sensor information to point cloud datagenerator 4618. Point cloud data generator 4618 generates point clouddata from the sensor information, and outputs the point cloud data toencoder 4613.

Presenter 4612 presents the sensor information or point cloud data to auser. For example, presenter 4612 displays information or an image basedon the sensor information or point cloud data.

Encoder 4613 encodes (compresses) the point cloud data, and outputs theresulting encoded data, control information (signaling information)obtained in the course of the encoding, and other additional informationto multiplexer 4614. The additional information includes the sensorinformation, for example.

Multiplexer 4614 generates multiplexed data by multiplexing the encodeddata, the control information, and the additional information inputthereto from encoder 4613. A format of the multiplexed data is a fileformat for accumulation or a packet format for transmission, forexample.

Input/output unit 4615 (a communication unit or interface, for example)outputs the multiplexed data to the outside. Alternatively, themultiplexed data may be accumulated in an accumulator, such as aninternal memory. Controller 4616 (or an application executor) controlseach processor. That is, controller 4616 controls the encoding, themultiplexing, or other processing.

Note that the sensor information may be input to encoder 4613 ormultiplexer 4614. Alternatively, input/output unit 4615 may output thepoint cloud data or encoded data to the outside as it is.

A transmission signal (multiplexed data) output from three-dimensionaldata encoding system 4601 is input to three-dimensional data decodingsystem 4602 via external connector 4604.

Three-dimensional data decoding system 4602 generates point cloud data,which is three-dimensional data, by decoding the encoded data ormultiplexed data. Note that three-dimensional data decoding system 4602may be a three-dimensional data decoding device implemented by a singledevice or a system implemented by a plurality of devices. Thethree-dimensional data decoding device may include a part of a pluralityof processors included in three-dimensional data decoding system 4602.

Three-dimensional data decoding system 4602 includes sensor informationobtainer 4621, input/output unit 4622, demultiplexer 4623, decoder 4624,presenter 4625, user interface 4626, and controller 4627.

Sensor information obtainer 4621 obtains sensor information from sensorterminal 4603.

Input/output unit 4622 obtains the transmission signal, decodes thetransmission signal into the multiplexed data (file format or packet),and outputs the multiplexed data to demultiplexer 4623.

Demultiplexer 4623 obtains the encoded data, the control information,and the additional information from the multiplexed data, and outputsthe encoded data, the control information, and the additionalinformation to decoder 4624.

Decoder 4624 reconstructs the point cloud data by decoding the encodeddata.

Presenter 4625 presents the point cloud data to a user. For example,presenter 4625 displays information or an image based on the point clouddata. User interface 4626 obtains an indication based on a manipulationby the user. Controller 4627 (or an application executor) controls eachprocessor. That is, controller 4627 controls the demultiplexing, thedecoding, the presentation, or other processing. Note that input/outputunit 4622 may obtain the point cloud data or encoded data as it is fromthe outside. Presenter 4625 may obtain additional information, such assensor information, and present information based on the additionalinformation. Presenter 4625 may perform a presentation based on anindication from a user obtained on user interface 4626.

Sensor terminal 4603 generates sensor information, which is informationobtained by a sensor. Sensor terminal 4603 is a terminal provided with asensor or a camera. For example, sensor terminal 4603 is a mobile body,such as an automobile, a flying object, such as an aircraft, a mobileterminal, or a camera.

Sensor information that can be generated by sensor terminal 4603includes (1) the distance between sensor terminal 4603 and an object orthe reflectance of the object obtained by LiDAR, a millimeter waveradar, or an infrared sensor or (2) the distance between a camera and anobject or the reflectance of the object obtained by a plurality ofmonocular camera images or a stereo-camera image, for example. Thesensor information may include the posture, orientation, gyro (angularvelocity), position (GPS information or altitude), velocity, oracceleration of the sensor, for example. The sensor information mayinclude air temperature, air pressure, air humidity, or magnetism, forexample.

External connector 4604 is implemented by an integrated circuit (LSI orIC), an external accumulator, communication with a cloud server via theInternet, or broadcasting, for example.

Next, point cloud data will be described. FIG. 2 is a diagram showing aconfiguration of point cloud data. FIG. 3 is a diagram showing aconfiguration example of a data file describing information of the pointcloud data.

Point cloud data includes data on a plurality of points. Data on eachpoint includes geometry information (three-dimensional coordinates) andattribute information associated with the geometry information. A set ofa plurality of such points is referred to as a point cloud. For example,a point cloud indicates a three-dimensional shape of an object.

Geometry information (position), such as three-dimensional coordinates,may be referred to as geometry. Data on each point may include attributeinformation (attribute) on a plurality of types of attributes. A type ofattribute is color or reflectance, for example.

One item of attribute information (in other words, a piece of attributeinformation or an attribute information item) may be associated with oneitem of geometry information (in other words, a piece of geometryinformation or a geometry information item), or attribute information ona plurality of different types of attributes may be associated with oneitem of geometry information. Alternatively, items of attributeinformation on the same type of attribute may be associated with oneitem of geometry information.

The configuration example of a data file shown in FIG. 3 is an examplein which geometry information and attribute information are associatedwith each other in a one-to-one relationship, and geometry informationand attribute information on N points forming point cloud data areshown.

The geometry information is information on three axes, specifically, anx-axis, a y-axis, and a z-axis, for example. The attribute informationis RGB color information, for example. A representative data file is plyfile, for example.

Next, types of point cloud data will be described. FIG. 4 is a diagramshowing types of point cloud data. As shown in FIG. 4 , point cloud dataincludes a static object and a dynamic object.

The static object is three-dimensional point cloud data at an arbitrarytime (a time point). The dynamic object is three-dimensional point clouddata that varies with time. In the following, three-dimensional pointcloud data associated with a time point will be referred to as a PCCframe or a frame.

The object may be a point cloud whose range is limited to some extent,such as ordinary video data, or may be a large point cloud whose rangeis not limited, such as map information.

There are point cloud data having varying densities. There may be sparsepoint cloud data and dense point cloud data.

In the following, each processor will be described in detail. Sensorinformation is obtained by various means, including a distance sensorsuch as LiDAR or a range finder, a stereo camera, or a combination of aplurality of monocular cameras. Point cloud data generator 4618generates point cloud data based on the sensor information obtained bysensor information obtainer 4617. Point cloud data generator 4618generates geometry information as point cloud data, and adds attributeinformation associated with the geometry information to the geometryinformation.

When generating geometry information or adding attribute information,point cloud data generator 4618 may process the point cloud data. Forexample, point cloud data generator 4618 may reduce the data amount byomitting a point cloud whose position coincides with the position ofanother point cloud. Point cloud data generator 4618 may also convertthe geometry information (such as shifting, rotating or normalizing theposition) or render the attribute information.

Note that, although FIG. 1 shows point cloud data generation system 4611as being included in three-dimensional data encoding system 4601, pointcloud data generation system 4611 may be independently provided outsidethree-dimensional data encoding system 4601.

Encoder 4613 generates encoded data by encoding point cloud dataaccording to an encoding method previously defined. In general, thereare the two types of encoding methods described below. One is anencoding method using geometry information, which will be referred to asa first encoding method, hereinafter. The other is an encoding methodusing a video codec, which will be referred to as a second encodingmethod, hereinafter.

Decoder 4624 decodes the encoded data into the point cloud data usingthe encoding method previously defined.

Multiplexer 4614 generates multiplexed data by multiplexing the encodeddata in an existing multiplexing method. The generated multiplexed datais transmitted or accumulated. Multiplexer 4614 multiplexes not only thePCC-encoded data but also another medium, such as a video, an audio,subtitles, an application, or a file, or reference time information.Multiplexer 4614 may further multiplex attribute information associatedwith sensor information or point cloud data.

Multiplexing schemes or file formats include ISOBMFF, MPEG-DASH, whichis a transmission scheme based on ISOBMFF, MMT, MPEG-2 TS Systems, orRMP, for example.

Demultiplexer 4623 extracts PCC-encoded data, other media, timeinformation and the like from the multiplexed data.

Input/output unit 4615 transmits the multiplexed data in a methodsuitable for the transmission medium or accumulation medium, such asbroadcasting or communication. Input/output unit 4615 may communicatewith another device over the Internet or communicate with anaccumulator, such as a cloud server.

As a communication protocol, http, ftp, TCP, UDP or the like is used.The pull communication scheme or the push communication scheme can beused.

A wired transmission or a wireless transmission can be used. For thewired transmission, Ethernet (registered trademark), USB, RS-232C, HDMI(registered trademark), or a coaxial cable is used, for example. For thewireless transmission, wireless LAN, Wi-Fi (registered trademark),Bluetooth (registered trademark), or a millimeter wave is used, forexample.

As a broadcasting scheme, DVB-T2, DVB-S2, DVB-C2, ATSC3.0, or ISDB-S3 isused, for example.

FIG. 5 is a diagram showing a configuration of first encoder 4630, whichis an example of encoder 4613 that performs encoding in the firstencoding method. FIG. 6 is a block diagram showing first encoder 4630.First encoder 4630 generates encoded data (encoded stream) by encodingpoint cloud data in the first encoding method. First encoder 4630includes geometry information encoder 4631, attribute informationencoder 4632, additional information encoder 4633, and multiplexer 4634.

First encoder 4630 is characterized by performing encoding by keeping athree-dimensional structure in mind. First encoder 4630 is furthercharacterized in that attribute information encoder 4632 performsencoding using information obtained from geometry information encoder4631. The first encoding method is referred to also as geometry-basedPCC (GPCC).

Point cloud data is PCC point cloud data like a PLY file or PCC pointcloud data generated from sensor information, and includes geometryinformation (position), attribute information (attribute), and otheradditional information (metadata). The geometry information is input togeometry information encoder 4631, the attribute information is input toattribute information encoder 4632, and the additional information isinput to additional information encoder 4633.

Geometry information encoder 4631 generates encoded geometry information(compressed geometry), which is encoded data, by encoding geometryinformation. For example, geometry information encoder 4631 encodesgeometry information using an N-ary tree structure, such as an octree.Specifically, in the case of an octree, a current space (target space)is divided into eight nodes (subspaces), 8-bit information (occupancycode) that indicates whether each node includes a point cloud or not isgenerated. A node including a point cloud is further divided into eightnodes, and 8-bit information that indicates whether each of the eightnodes includes a point cloud or not is generated. This process isrepeated until a predetermined level is reached or the number of thepoint clouds included in each node becomes equal to or less than athreshold.

Attribute information encoder 4632 generates encoded attributeinformation (compressed attribute), which is encoded data, by encodingattribute information using configuration information generated bygeometry information encoder 4631. For example, attribute informationencoder 4632 determines a reference point (reference node) that is to bereferred to in encoding a current point (in other words, a current nodeor a target node) to be processed based on the octree structuregenerated by geometry information encoder 4631. For example, attributeinformation encoder 4632 refers to a node whose parent node in theoctree is the same as the parent node of the current node, of peripheralnodes or neighboring nodes. Note that the method of determining areference relationship is not limited to this method.

The process of encoding attribute information may include at least oneof a quantization process, a prediction process, and an arithmeticencoding process. In this case, “refer to” means using a reference nodefor calculating a predicted value of attribute information or using astate of a reference node (occupancy information that indicates whethera reference node includes a point cloud or not, for example) fordetermining a parameter of encoding. For example, the parameter ofencoding is a quantization parameter in the quantization process or acontext or the like in the arithmetic encoding.

Additional information encoder 4633 generates encoded additionalinformation (compressed metadata), which is encoded data, by encodingcompressible data of additional information.

Multiplexer 4634 generates encoded stream (compressed stream), which isencoded data, by multiplexing encoded geometry information, encodedattribute information, encoded additional information, and otheradditional information. The generated encoded stream is output to aprocessor in a system layer (not shown).

Next, first decoder 4640, which is an example of decoder 4624 thatperforms decoding in the first encoding method, will be described. FIG.7 is a diagram showing a configuration of first decoder 4640. FIG. 8 isa block diagram showing first decoder 4640. First decoder 4640 generatespoint cloud data by decoding encoded data (encoded stream) encoded inthe first encoding method in the first encoding method. First decoder4640 includes demultiplexer 4641, geometry information decoder 4642,attribute information decoder 4643, and additional information decoder4644.

An encoded stream (compressed stream), which is encoded data, is inputto first decoder 4640 from a processor in a system layer (not shown).

Demultiplexer 4641 separates encoded geometry information (compressedgeometry), encoded attribute information (compressed attribute), encodedadditional information (compressed metadata), and other additionalinformation from the encoded data.

Geometry information decoder 4642 generates geometry information bydecoding the encoded geometry information. For example, geometryinformation decoder 4642 restores the geometry information on a pointcloud represented by three-dimensional coordinates from encoded geometryinformation represented by an N-ary structure, such as an octree.

Attribute information decoder 4643 decodes the encoded attributeinformation based on configuration information generated by geometryinformation decoder 4642. For example, attribute information decoder4643 determines a reference point (reference node) that is to bereferred to in decoding a current point (current node) to be processedbased on the octree structure generated by geometry information decoder4642. For example, attribute information decoder 4643 refers to a nodewhose parent node in the octree is the same as the parent node of thecurrent node, of peripheral nodes or neighboring nodes. Note that themethod of determining a reference relationship is not limited to thismethod.

The process of decoding attribute information may include at least oneof an inverse quantization process, a prediction process, and anarithmetic decoding process. In this case, “refer to” means using areference node for calculating a predicted value of attributeinformation or using a state of a reference node (occupancy informationthat indicates whether a reference node includes a point cloud or not,for example) for determining a parameter of decoding. For example, theparameter of decoding is a quantization parameter in the inversequantization process or a context or the like in the arithmeticdecoding.

Additional information decoder 4644 generates additional information bydecoding the encoded additional information. First decoder 4640 usesadditional information required for the decoding process for thegeometry information and the attribute information in the decoding, andoutputs additional information required for an application to theoutside.

Next, an example configuration of a geometry information encoder will bedescribed. FIG. 9 is a block diagram of geometry information encoder2700 according to this embodiment. Geometry information encoder 2700includes octree generator 2701, geometry information calculator 2702,encoding table selector 2703, and entropy encoder 2704.

Octree generator 2701 generates an octree, for example, from inputposition information, and generates an occupancy code of each node ofthe octree. Geometry information calculator 2702 obtains informationthat indicates whether a neighboring node of a current node (targetnode) is an occupied node or not. For example, geometry informationcalculator 2702 calculates occupancy information on a neighboring nodefrom an occupancy code of a parent node to which a current node belongs(information that indicates whether a neighboring node is an occupiednode or not). Geometry information calculator 2702 may save an encodednode in a list and search the list for a neighboring node. Note thatgeometry information calculator 2702 may change neighboring nodes inaccordance with the position of the current node in the parent node.

Encoding table selector 2703 selects an encoding table used for entropyencoding of the current node based on the occupancy information on theneighboring node calculated by geometry information calculator 2702. Forexample, encoding table selector 2703 may generate a bit sequence basedon the occupancy information on the neighboring node and select anencoding table of an index number generated from the bit sequence.

Entropy encoder 2704 generates encoded geometry information and metadataby entropy-encoding the occupancy code of the current node using theencoding table of the selected index number. Entropy encoder may add, tothe encoded geometry information, information that indicates theselected encoding table.

In the following, an octree representation and a scan order for geometryinformation will be described. Geometry information (geometry data) istransformed into an octree structure (octree transform) and thenencoded. The octree structure includes nodes and leaves. Each node haseight nodes or leaves, and each leaf has voxel (VXL) information. FIG.10 is a diagram showing an example structure of geometry informationincluding a plurality of voxels. FIG. 11 is a diagram showing an examplein which the geometry information shown in FIG. 10 is transformed intoan octree structure. Here, of leaves shown in FIG. 11 , leaves 1, 2, and3 represent voxels VXL1, VXL2, and VXL3 shown in FIG. 10 , respectively,and each represent VXL containing a point cloud (referred to as a validVXL, hereinafter).

Specifically, node 1 corresponds to the entire space comprising thegeometry information in FIG. 10 . The entire space corresponding to node1 is divided into eight nodes, and among the eight nodes, a nodecontaining valid VXL is further divided into eight nodes or leaves. Thisprocess is repeated for every layer of the tree structure. Here, eachnode corresponds to a subspace, and has information (occupancy code)that indicates where the next node or leaf is located after division asnode information. A block in the bottom layer is designated as a leafand retains the number of the points contained in the leaf as leafinformation.

Next, an example configuration of a geometry information decoder will bedescribed. FIG. 12 is a block diagram of geometry information decoder2710 according to this embodiment. Geometry information decoder 2710includes octree generator 2711, geometry information calculator 2712,encoding table selector 2713, and entropy decoder 2714.

Octree generator 2711 generates an octree of a space (node) based onheader information, metadata or the like of a bitstream. For example,octree generator 2711 generates an octree by generating a large space(root node) based on the sizes of a space in an x-axis direction, ay-axis direction, and a z-axis direction added to the header informationand dividing the space into two parts in the x-axis direction, they-axis direction, and the z-axis direction to generate eight smallspaces A (nodes A0 to A7). Nodes A0 to A7 are sequentially designated asa current node.

Geometry information calculator 2712 obtains occupancy information thatindicates whether a neighboring node of a current node is an occupiednode or not. For example, geometry information calculator 2712calculates occupancy information on a neighboring node from an occupancycode of a parent node to which a current node belongs. Geometryinformation calculator 2712 may save a decoded node in a list and searchthe list for a neighboring node. Note that geometry informationcalculator 2712 may change neighboring nodes in accordance with theposition of the current node in the parent node.

Encoding table selector 2713 selects an encoding table (decoding table)used for entropy decoding of the current node based on the occupancyinformation on the neighboring node calculated by geometry informationcalculator 2712. For example, encoding table selector 2713 may generatea bit sequence based on the occupancy information on the neighboringnode and select an encoding table of an index number generated from thebit sequence.

Entropy decoder 2714 generates position information by entropy-decodingthe occupancy code of the current node using the selected encodingtable. Note that entropy decoder 2714 may obtain information on theselected encoding table by decoding the bitstream, and entropy-decodethe occupancy code of the current node using the encoding tableindicated by the information.

In the following, configurations of an attribute information encoder andan attribute information decoder will be described. FIG. 13 is a blockdiagram showing an example configuration of attribute informationencoder A100. The attribute information encoder may include a pluralityof encoders that perform different encoding methods. For example, theattribute information encoder may selectively use any of the two methodsdescribed below in accordance with the use case.

Attribute information encoder A100 includes LoD attribute informationencoder A101 and transformed-attribute-information encoder A102. LoDattribute information encoder A101 classifies three-dimensional pointsinto a plurality of layers based on geometry information on thethree-dimensional points, predicts attribute information onthree-dimensional points belonging to each layer, and encodes aprediction residual therefor. Here, each layer into which athree-dimensional point is classified is referred to as a level ofdetail (LoD).

Transformed-attribute-information encoder A102 encodes attributeinformation using region adaptive hierarchical transform (RAHT).Specifically, transformed-attribute-information encoder A102 generates ahigh frequency component and a low frequency component for each layer byapplying RAHT or Haar transform to each item of attribute informationbased on the geometry information on three-dimensional points, andencodes the values by quantization, entropy encoding or the like.

FIG. 14 is a block diagram showing an example configuration of attributeinformation decoder A110. The attribute information decoder may includea plurality of decoders that perform different decoding methods. Forexample, the attribute information decoder may selectively use any ofthe two methods described below for decoding based on the informationincluded in the header or metadata.

Attribute information decoder A110 includes LoD attribute informationdecoder A111 and transformed-attribute-information decoder A112. LoDattribute information decoder A111 classifies three-dimensional pointsinto a plurality of layers based on the geometry information on thethree-dimensional points, predicts attribute information onthree-dimensional points belonging to each layer, and decodes attributevalues thereof.

Transformed-attribute-information decoder A112 decodes attributeinformation using region adaptive hierarchical transform (RAHT).Specifically, transformed-attribute-information decoder A112 decodeseach attribute value by applying inverse RAHT or inverse Haar transformto the high frequency component and the low frequency component of theattribute value based on the geometry information on thethree-dimensional point.

FIG. 15 is a block diagram showing a configuration of attributeinformation encoder 3140 that is an example of LoD attribute informationencoder A101.

Attribute information encoder 3140 includes LoD generator 3141,periphery searcher 3142, predictor 3143, prediction residual calculator3144, quantizer 3145, arithmetic encoder 3146, inverse quantizer 3147,decoded value generator 3148, and memory 3149.

LoD generator 3141 generates an LoD using geometry information on athree-dimensional point.

Periphery searcher 3142 searches for a neighboring three-dimensionalpoint neighboring each three-dimensional point using a result of LoDgeneration by LoD generator 3141 and distance information indicatingdistances between three-dimensional points.

Predictor 3143 generates a predicted value of an item of attributeinformation on a current (target) three-dimensional point to be encoded.

Prediction residual calculator 3144 calculates (generates) a predictionresidual of the predicted value of the item of the attribute informationgenerated by predictor 3143.

Quantizer 3145 quantizes the prediction residual of the item ofattribute information calculated by prediction residual calculator 3144.

Arithmetic encoder 3146 arithmetically encodes the prediction residualquantized by quantizer 3145. Arithmetic encoder 3146 outputs a bitstreamincluding the arithmetically encoded prediction residual to thethree-dimensional data decoding device, for example.

The prediction residual may be binarized by quantizer 3145 before beingarithmetically encoded by arithmetic encoder 3146.

Arithmetic encoder 3146 may initialize the encoding table used for thearithmetic encoding before performing the arithmetic encoding.Arithmetic encoder 3146 may initialize the encoding table used for thearithmetic encoding for each layer. Arithmetic encoder 3146 may output abitstream including information that indicates the position of the layerat which the encoding table is initialized.

Inverse quantizer 3147 inverse-quantizes the prediction residualquantized by quantizer 3145.

Decoded value generator 3148 generates a decoded value by adding thepredicted value of the item of attribute information generated bypredictor 3143 and the prediction residual inverse-quantized by inversequantizer 3147 together.

Memory 3149 is a memory that stores a decoded value of an item ofattribute information on each three-dimensional point decoded by decodedvalue generator 3148. For example, when generating a predicted value ofa three-dimensional point yet to be encoded, predictor 3143 may generatethe predicted value using a decoded value of an item of attributeinformation on each three-dimensional point stored in memory 3149.

FIG. 16 is a block diagram of attribute information encoder 6600 that isan example of transformation attribute information encoder A102.Attribute information encoder 6600 includes sorter 6601, Haartransformer 6602, quantizer 6603, inverse quantizer 6604, inverse Haartransformer 6605, memory 6606, and arithmetic encoder 6607.

Sorter 6601 generates the Morton codes by using the geometry informationof three-dimensional points, and sorts the plurality ofthree-dimensional points in the order of the Morton codes. Haartransformer 6602 generates the coding coefficient by applying the Haartransform to the attribute information. Quantizer 6603 quantizes thecoding coefficient of the attribute information.

Inverse quantizer 6604 inverse quantizes the coding coefficient afterthe quantization. Inverse Haar transformer 6605 applies the inverse Haartransform to the coding coefficient. Memory 6606 stores the values ofitems of attribute information of a plurality of decodedthree-dimensional points. For example, the attribute information of thedecoded three-dimensional points stored in memory 6606 may be utilizedfor prediction and the like of an unencoded three-dimensional point.

Arithmetic encoder 6607 calculates ZeroCnt from the coding coefficientafter the quantization, and arithmetically encodes ZeroCnt.Additionally, arithmetic encoder 6607 arithmetically encodes thenon-zero coding coefficient after the quantization. Arithmetic encoder6607 may binarize the coding coefficient before the arithmetic encoding.In addition, arithmetic encoder 6607 may generate and encode variouskinds of header information.

FIG. 17 is a block diagram showing a configuration of attributeinformation decoder 3150 that is an example of LoD attribute informationdecoder A111.

Attribute information decoder 3150 includes LoD generator 3151,periphery searcher 3152, predictor 3153, arithmetic decoder 3154,inverse quantizer 3155, decoded value generator 3156, and memory 3157.

LoD generator 3151 generates an LoD using geometry information on athree-dimensional point decoded by the geometry information decoder (notshown in FIG. 17 ).

Periphery searcher 3152 searches for a neighboring three-dimensionalpoint neighboring each three-dimensional point using a result of LoDgeneration by LoD generator 3151 and distance information indicatingdistances between three-dimensional points.

Predictor 3153 generates a predicted value of attribute information itemon a current three-dimensional point to be decoded.

Arithmetic decoder 3154 arithmetically decodes the prediction residualin the bitstream obtained from attribute information encoder 3140 shownin FIG. 15 . Note that arithmetic decoder 3154 may initialize thedecoding table used for the arithmetic decoding. Arithmetic decoder 3154initializes the decoding table used for the arithmetic decoding for thelayer for which the encoding process has been performed by arithmeticencoder 3146 shown in FIG. 15 . Arithmetic decoder 3154 may initializethe decoding table used for the arithmetic decoding for each layer.Arithmetic decoder 3154 may initialize the decoding table based on theinformation included in the bitstream that indicates the position of thelayer for which the encoding table has been initialized.

Inverse quantizer 3155 inverse-quantizes the prediction residualarithmetically decoded by arithmetic decoder 3154.

Decoded value generator 3156 generates a decoded value by adding thepredicted value generated by predictor 3153 and the prediction residualinverse-quantized by inverse quantizer 3155 together. Decoded valuegenerator 3156 outputs the decoded attribute information data to anotherdevice.

Memory 3157 is a memory that stores a decoded value of an item ofattribute information on each three-dimensional point decoded by decodedvalue generator 3156. For example, when generating a predicted value ofa three-dimensional point yet to be decoded, predictor 3153 generatesthe predicted value using a decoded value of an item of attributeinformation on each three-dimensional point stored in memory 3157.

FIG. 18 is a block diagram of attribute information decoder 6610 that isan example of transformation attribute information decoder A112.Attribute information decoder 6610 includes arithmetic decoder 6611,inverse quantizer 6612, inverse Haar transformer 6613, and memory 6614.

Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the codingcoefficient included in a bitstream. Note that arithmetic decoder 6611may decode various kinds of header information.

Inverse quantizer 6612 inverse quantizes the arithmetically decodedcoding coefficient. Inverse Haar transformer 6613 applies the inverseHaar transform to the coding coefficient after the inverse quantization.Memory 6614 stores the values of items of attribute information of aplurality of decoded three-dimensional points. For example, theattribute information of the decoded three-dimensional points stored inmemory 6614 may be utilized for prediction of an undecodedthree-dimensional point.

Next, second encoder 4650, which is an example of encoder 4613 thatperforms encoding in the second encoding method, will be described. FIG.19 is a diagram showing a configuration of second encoder 4650. FIG. 20is a block diagram showing second encoder 4650.

Second encoder 4650 generates encoded data (encoded stream) by encodingpoint cloud data in the second encoding method. Second encoder 4650includes additional information generator 4651, geometry image generator4652, attribute image generator 4653, video encoder 4654, additionalinformation encoder 4655, and multiplexer 4656.

Second encoder 4650 is characterized by generating a geometry image andan attribute image by projecting a three-dimensional structure onto atwo-dimensional image, and encoding the generated geometry image andattribute image in an existing video encoding scheme. The secondencoding method is referred to as video-based PCC (VPCC).

Point cloud data is PCC point cloud data like a PLY file or PCC pointcloud data generated from sensor information, and includes geometryinformation (position), attribute information (attribute), and otheradditional information (metadata).

Additional information generator 4651 generates map information on aplurality of two-dimensional images by projecting a three-dimensionalstructure onto a two-dimensional image.

Geometry image generator 4652 generates a geometry image based on thegeometry information and the map information generated by additionalinformation generator 4651. The geometry image is a distance image inwhich distance (depth) is indicated as a pixel value, for example. Thedistance image may be an image of a plurality of point clouds viewedfrom one point of view (an image of a plurality of point cloudsprojected onto one two-dimensional plane), a plurality of images of aplurality of point clouds viewed from a plurality of points of view, ora single image integrating the plurality of images.

Attribute image generator 4653 generates an attribute image based on theattribute information and the map information generated by additionalinformation generator 4651. The attribute image is an image in whichattribute information (color (RGB), for example) is indicated as a pixelvalue, for example. The image may be an image of a plurality of pointclouds viewed from one point of view (an image of a plurality of pointclouds projected onto one two-dimensional plane), a plurality of imagesof a plurality of point clouds viewed from a plurality of points ofview, or a single image integrating the plurality of images.

Video encoder 4654 generates an encoded geometry image (compressedgeometry image) and an encoded attribute image (compressed attributeimage), which are encoded data, by encoding the geometry image and theattribute image in a video encoding scheme. Note that, as the videoencoding scheme, any well-known encoding method can be used. Forexample, the video encoding scheme is AVC or HEVC.

Additional information encoder 4655 generates encoded additionalinformation (compressed metadata) by encoding the additionalinformation, the map information and the like included in the pointcloud data.

Multiplexer 4656 generates an encoded stream (compressed stream), whichis encoded data, by multiplexing the encoded geometry image, the encodedattribute image, the encoded additional information, and otheradditional information. The generated encoded stream is output to aprocessor in a system layer (not shown).

Next, second decoder 4660, which is an example of decoder 4624 thatperforms decoding in the second encoding method, will be described. FIG.21 is a diagram showing a configuration of second decoder 4660. FIG. 22is a block diagram showing second decoder 4660. Second decoder 4660generates point cloud data by decoding encoded data (encoded stream)encoded in the second encoding method in the second encoding method.Second decoder 4660 includes demultiplexer 4661, video decoder 4662,additional information decoder 4663, geometry information generator4664, and attribute information generator 4665.

An encoded stream (compressed stream), which is encoded data, is inputto second decoder 4660 from a processor in a system layer (not shown).

Demultiplexer 4661 separates an encoded geometry image (compressedgeometry image), an encoded attribute image (compressed attributeimage), an encoded additional information (compressed metadata), andother additional information from the encoded data.

Video decoder 4662 generates a geometry image and an attribute image bydecoding the encoded geometry image and the encoded attribute image in avideo encoding scheme. Note that, as the video encoding scheme, anywell-known encoding method can be used. For example, the video encodingscheme is AVC or HEVC.

Additional information decoder 4663 generates additional informationincluding map information or the like by decoding the encoded additionalinformation.

Geometry information generator 4664 generates geometry information fromthe geometry image and the map information. Attribute informationgenerator 4665 generates attribute information from the attribute imageand the map information.

Second decoder 4660 uses additional information required for decoding inthe decoding, and outputs additional information required for anapplication to the outside.

In the following, a problem with the PCC encoding scheme will bedescribed. FIG. 23 is a diagram showing a protocol stack relating toPCC-encoded data. FIG. 23 shows an example in which PCC-encoded data ismultiplexed with other medium data, such as a video (HEVC, for example)or an audio, and transmitted or accumulated.

A multiplexing scheme and a file format have a function of multiplexingvarious encoded data and transmitting or accumulating the data. Totransmit or accumulate encoded data, the encoded data has to beconverted into a format for the multiplexing scheme. For example, withHEVC, a technique for storing encoded data in a data structure referredto as a NAL unit and storing the NAL unit in ISOBMFF is prescribed.

At present, a first encoding method (Codec1) and a second encodingmethod (Codec2) are under investigation as encoding methods for pointcloud data. However, there is no method defined for storing theconfiguration of encoded data and the encoded data in a system format.Thus, there is a problem that an encoder cannot perform an MUX process(multiplexing), transmission, or accumulation of data.

Note that, in the following, the term “encoding method” means any of thefirst encoding method and the second encoding method unless a particularencoding method is specified.

Embodiment 2

In this embodiment, types of the encoded data (geometry information(geometry), attribute information (attribute), and additionalinformation (metadata)) generated by first encoder 4630 or secondencoder 4650 described above, a method of generating additionalinformation (metadata), and a multiplexing process in the multiplexerwill be described. The additional information (metadata) may be referredto as a parameter set or control information (signaling information).

In this embodiment, the dynamic object (three-dimensional point clouddata that varies with time) described above with reference to FIG. 4will be described, for example. However, the same method can also beused for the static object (three-dimensional point cloud dataassociated with an arbitrary time point).

FIG. 24 is a diagram showing configurations of encoder 4801 andmultiplexer 4802 in a three-dimensional data encoding device accordingto this embodiment. Encoder 4801 corresponds to first encoder 4630 orsecond encoder 4650 described above, for example. Multiplexer 4802corresponds to multiplexer 4634 or 4656 described above.

Encoder 4801 encodes a plurality of PCC (point cloud compression) framesof point cloud data to generate a plurality of pieces of encoded data(multiple compressed data) of geometry information, attributeinformation, and additional information.

Multiplexer 4802 integrates a plurality of types of data (geometryinformation, attribute information, and additional information) into aNAL unit, thereby converting the data into a data configuration thattakes data access in the decoding device into consideration.

FIG. 25 is a diagram showing a configuration example of the encoded datagenerated by encoder 4801. Arrows in the drawing indicate a dependenceinvolved in decoding of the encoded data. The source of an arrow dependson data of the destination of the arrow. That is, the decoding devicedecodes the data of the destination of an arrow, and decodes the data ofthe source of the arrow using the decoded data. In other words, “a firstentity depends on a second entity” means that data of the second entityis referred to (used) in processing (encoding, decoding, or the like) ofdata of the first entity.

First, a process of generating encoded data of geometry information willbe described. Encoder 4801 encodes geometry information of each frame togenerate encoded geometry data (compressed geometry data) for eachframe. The encoded geometry data is denoted by G(i). i denotes a framenumber or a time point of a frame, for example.

Furthermore, encoder 4801 generates a geometry parameter set (GPS(i))for each frame. The geometry parameter set includes a parameter that canbe used for decoding of the encoded geometry data. The encoded geometrydata for each frame depends on an associated geometry parameter set.

The encoded geometry data formed by a plurality of frames is defined asa geometry sequence. Encoder 4801 generates a geometry sequenceparameter set (referred to also as geometry sequence PS or geometry SPS)that stores a parameter commonly used for a decoding process for theplurality of frames in the geometry sequence. The geometry sequencedepends on the geometry SPS.

Next, a process of generating encoded data of attribute information willbe described. Encoder 4801 encodes attribute information of each frameto generate encoded attribute data (compressed attribute data) for eachframe. The encoded attribute data is denoted by A(i). FIG. 25 shows anexample in which there are attribute X and attribute Y, and encodedattribute data for attribute X is denoted by AX(i), and encodedattribute data for attribute Y is denoted by AY(i).

Furthermore, encoder 4801 generates an attribute parameter set (APS(i))for each frame. The attribute parameter set for attribute X is denotedby AXPS(i), and the attribute parameter set for attribute Y is denotedby AYPS(i). The attribute parameter set includes a parameter that can beused for decoding of the encoded attribute information. The encodedattribute data depends on an associated attribute parameter set.

The encoded attribute data formed by a plurality of frames is defined asan attribute sequence. Encoder 4801 generates an attribute sequenceparameter set (referred to also as attribute sequence PS or attributeSPS) that stores a parameter commonly used for a decoding process forthe plurality of frames in the attribute sequence. The attributesequence depends on the attribute SPS.

In the first encoding method, the encoded attribute data depends on theencoded geometry data.

FIG. 25 shows an example in which there are two types of attributeinformation (attribute X and attribute Y). When there are two types ofattribute information, for example, two encoders generate data andmetadata for the two types of attribute information. For example, anattribute sequence is defined for each type of attribute information,and an attribute SPS is generated for each type of attributeinformation.

Note that, although FIG. 25 shows an example in which there is one typeof geometry information, and there are two types of attributeinformation, the present disclosure is not limited thereto. There may beone type of attribute information or three or more types of attributeinformation. In such cases, encoded data can be generated in the samemanner. If the point cloud data has no attribute information, there maybe no attribute information. In such a case, encoder 4801 does not haveto generate a parameter set associated with attribute information.

Next, a process of generating encoded data of additional information(metadata) will be described. Encoder 4801 generates a PCC stream PS(referred to also as PCC stream PS or stream PS), which is a parameterset for the entire PCC stream. Encoder 4801 stores a parameter that canbe commonly used for a decoding process for one or more geometrysequences and one or more attribute sequences in the stream PS. Forexample, the stream PS includes identification information indicatingthe codec for the point cloud data and information indicating analgorithm used for the encoding, for example. The geometry sequence andthe attribute sequence depend on the stream PS.

Next, an access unit and a GOF will be described. In this embodiment,concepts of access unit (AU) and group of frames (GOF) are newlyintroduced.

An access unit is a basic unit for accessing data in decoding, and isformed by one or more pieces of data and one or more pieces of metadata.For example, an access unit is formed by geometry information and one ormore pieces of attribute information associated with a same time point.A GOF is a random access unit, and is formed by one or more accessunits.

Encoder 4801 generates an access unit header (AU header) asidentification information indicating the top of an access unit. Encoder4801 stores a parameter relating to the access unit in the access unitheader. For example, the access unit header includes a configuration ofor information on the encoded data included in the access unit. Theaccess unit header further includes a parameter commonly used for thedata included in the access unit, such as a parameter relating todecoding of the encoded data.

Note that encoder 4801 may generate an access unit delimiter thatincludes no parameter relating to the access unit, instead of the accessunit header. The access unit delimiter is used as identificationinformation indicating the top of the access unit. The decoding deviceidentifies the top of the access unit by detecting the access unitheader or the access unit delimiter.

Next, generation of identification information for the top of a GOF willbe described. As identification information indicating the top of a GOF,encoder 4801 generates a GOF header. Encoder 4801 stores a parameterrelating to the GOF in the GOF header. For example, the GOF headerincludes a configuration of or information on the encoded data includedin the GOF. The GOF header further includes a parameter commonly usedfor the data included in the GOF, such as a parameter relating todecoding of the encoded data.

Note that encoder 4801 may generate a GOF delimiter that includes noparameter relating to the GOF, instead of the GOF header. The GOFdelimiter is used as identification information indicating the top ofthe GOF. The decoding device identifies the top of the GOF by detectingthe GOF header or the GOF delimiter.

In the PCC-encoded data, the access unit is defined as a PCC frame unit,for example. The decoding device accesses a PCC frame based on theidentification information for the top of the access unit.

For example, the GOF is defined as one random access unit. The decodingdevice accesses a random access unit based on the identificationinformation for the top of the GOF. For example, if PCC frames areindependent from each other and can be separately decoded, a PCC framecan be defined as a random access unit.

Note that two or more PCC frames may be assigned to one access unit, anda plurality of random access units may be assigned to one GOF.

Encoder 4801 may define and generate a parameter set or metadata otherthan those described above. For example, encoder 4801 may generatesupplemental enhancement information (SEI) that stores a parameter (anoptional parameter) that is not always used for decoding.

Next, a configuration of encoded data and a method of storing encodeddata in a NAL unit will be described.

For example, a data format is defined for each type of encoded data.FIG. 26 is a diagram showing an example of encoded data and a NAL unit.

For example, as shown in FIG. 26 , encoded data includes a header and apayload. The encoded data may include length information indicating thelength (data amount) of the encoded data, the header, or the payload.The encoded data may include no header.

The header includes identification information for identifying the data,for example. The identification information indicates a data type or aframe number, for example.

The header includes identification information indicating a referencerelationship, for example. The identification information is stored inthe header when there is a dependence relationship between data, forexample, and allows an entity to refer to another entity. For example,the header of the entity to be referred to includes identificationinformation for identifying the data. The header of the referring entityincludes identification information indicating the entity to be referredto.

Note that, when the entity to be referred to or the referring entity canbe identified or determined from other information, the identificationinformation for identifying the data or identification informationindicating the reference relationship can be omitted.

Multiplexer 4802 stores the encoded data in the payload of the NAL unit.The NAL unit header includes pcc_nal_unit_type, which is identificationinformation for the encoded data. FIG. 27 is a diagram showing asemantics example of pcc_nal_unit_type.

As shown in FIG. 27 , when pcc_codec_type is codec 1 (Codec1: firstencoding method), values 0 to 10 of pcc_nal_unit_type are assigned toencoded geometry data (Geometry), encoded attribute X data (AttributeX),encoded attribute Y data (AttributeY), geometry PS (Geom. PS), attributeXPS (AttrX. S), attribute YPS (AttrY. PS), geometry SPS (GeometrySequence PS), attribute X SPS (AttributeX Sequence PS), attribute Y SPS(AttributeY Sequence PS), AU header (AU Header), and GOF header (GOFHeader) in codec 1. Values of 11 and greater are reserved in codec 1.

When pcc_codec_type is codec 2 (Codec2: second encoding method), valuesof 0 to 2 of pcc_nal_unit_type are assigned to data A (DataA), metadataA (MetaDataA), and metadata B (MetaDataB) in the codec. Values of 3 andgreater are reserved in codec 2.

Embodiment 3

In this embodiment, inter prediction of geometry information (an octree)on a point cloud will be described. FIG. 28 is a block diagram ofthree-dimensional data encoding device 12800 according to thisembodiment. Although FIG. 28 shows a processor involved in the encodingof geometry information (geometry) on point clouds, three-dimensionaldata encoding device 12800 may include other processors, such as aprocessor that encodes attribute information on point clouds. In interprediction, a point cloud to be encoded is encoded with reference to apreviously encoded point cloud.

Three-dimensional data encoding device 12800 includes octree generator12801, buffer 12802, entropy encoder 12803, buffer 12804, buffer 12805,point cloud generator 12806, buffer 12807, motion detector andcompensator 12808, octree generator 12809, buffer 12810, and controller12811.

Octree generator 12801 transforms a current point cloud, which is dataon an input point cloud to be encoded, into an octree representation,thereby generating a current octree that represents geometry informationon the current point cloud as an octree. In the input current pointcloud, a position in the point cloud is expressed as, for example,three-dimensional coordinates (e.g., x, y, z). Buffer 12802 holds thecurrent octree generated. An octree includes multiple nodes (branchpoints), each having information that includes an 8-bit occupancy codeindicating whether each of eight child nodes of that node includesthree-dimensional points. Buffer 12802 may initialize data held therein,for example for each octree (current point cloud).

Entropy encoder 12803 entropy-encodes information (e.g., the occupancycode) on each node to generate a bitstream. In this entropy encoding, aprobability parameter (also referred to as a coding table or aprobability table) is controlled based on information on nodes in thecurrent point cloud (intra-reference nodes) or information on nodes inan encoded point cloud (inter-reference nodes).

Buffer 12804 holds, as an intra-reference node (an encoded node),information (e.g., the occupancy code) on the current node. Buffer 12804may initialize data held therein, for example for each octree (currentpoint cloud).

Buffer 12805 holds information (e.g., the occupancy code) on the currentnode. Buffer 12805 also holds, as an encoded octree, information on thecurrent node on an octree basis. Buffer 12805 may initialize data heldtherein, for example for each octree (current point cloud).

Point cloud generator 12806 transforms the encoded octree into a pointcloud to generate an inter-reference point cloud (an encoded pointcloud). Buffer 12807 holds the inter-reference point cloud. That is,buffer 12807 holds multiple inter-reference point clouds, which may beone or more encoded point clouds.

Motion detector and compensator 12808 detects a displacement between aninter-reference point cloud and the current point cloud (motiondetection), and corrects the inter-reference point cloud based on thedisplacement detected (motion compensation). Motion detector andcompensator 12808 thus generates an aligned point cloud, which is theinter-reference point cloud aligned.

Octree generator 12809 transforms the aligned point cloud into an octreerepresentation to generate an inter-reference octree, which representsgeometry information on the aligned point cloud as an octree. Buffer12810 holds the inter-reference octree generated. Buffer 12810 mayinitialize data held therein, for example for each octree (current pointcloud).

Three-dimensional data encoding device 12800 may perform the motiondetection and the motion compensation on a frame or octree basis, or ona node (point) basis. Three-dimensional data encoding device 12800 mayincorporate motion compensation information, such as motion vectors,into the header of the frame or octree, or may incorporate theinformation into the header of the node information afterentropy-encoding the information.

The inter-reference point cloud may be a point cloud in an encoded framedifferent from the frame being encoded, or may be an encoded point cloudin the frame being encoded.

Controller 12811 uses intra-reference nodes in buffer 12804 orinter-reference nodes in the inter-reference octree in buffer 12810 tocontrol the probability parameter used by entropy encoder 12803 forentropy encoding (arithmetic encoding) of the current node. Whether theprobability parameter is controlled using intra-reference nodes(hereafter referred to as intra reference) or using inter-referencenodes (hereafter referred to as inter reference) may be predetermined,for example for each frame or point cloud, or may be determined in anymanner. For example, actual code amounts may be tentatively calculatedto select a reference scheme (intra reference or inter reference) thatyields a smaller code amount.

For example, if intra reference is used, the probability parameter isselected from multiple probability parameters based on the occupancystate of each of neighboring nodes (intra-reference nodes) of thecurrent node (whether the neighboring node includes points). If interreference is used, the probability parameter is selected from multipleprobability parameters based on the occupancy state of each of nodes(inter-reference nodes) at the same locations in the inter-referenceoctree as at least one of the current node and the neighboring nodes. Ifinter reference is selected, the probability parameter may be controlledby combining inter reference and intra reference. The multipleprobability parameters may include a probability parameter updatedaccording to the frequency of occurrence, or may include a fixed value.

Three-dimensional data encoding device 12800 may thus control theprobability parameter for entropy encoding based on information oninter-reference nodes in addition to information on intra-referencenodes. This can improve the accuracy of predicting the probability ofthe occurrence of information on the current node, and thus will improvethe coding efficiency.

Three-dimensional data encoding device 12800 does not need to alwaysrefer to the inter-reference point cloud. Rather, three-dimensional dataencoding device 12800 may encode the current point cloud based on onlyinformation on the current point cloud. This may be done by clearingbuffer 12807 storing inter-reference point clouds at predetermined timeintervals (e.g., every second), at predetermined frame intervals (e.g.,every 30 frames), or at any time when notifying the three-dimensionaldata decoding device. In this manner, the three-dimensional datadecoding device is allowed to start random replay at a point cloud thatis located at a position other than the beginning of the bitstream andthat does not refer to any inter-reference point cloud. This willimprove the random accessibility and error resistance of the bitstream.

FIG. 29 is a block diagram of three-dimensional data decoding device12820 according to this embodiment. Although FIG. 29 shows a processorinvolved in the decoding of geometry information (geometry) on pointclouds, three-dimensional data decoding device 12820 may include otherprocessors, such as a processor that decodes attribute information onpoint clouds. Three-dimensional data decoding device 12820 performsinter-prediction decoding, in which a point cloud is decoded from abitstream encoded with reference to encoded point clouds. For example,three-dimensional data decoding device 12820 decodes a bitstreamgenerated by three-dimensional data encoding device 12800 illustrated inFIG. 28 .

Three-dimensional data decoding device 12820 includes entropy decoder12821, buffer 12822, buffer 12823, point cloud generator 12824, buffer12825, motion compensator 12826, octree generator 12827, buffer 12828,and controller 12829.

Entropy decoder 12821 entropy-decodes an input bitstream for each branchpoint (node) of an octree to generate information (e.g., the occupancycode) on the decoded node. In this entropy decoding, a probabilityparameter (also referred to as a coding table or a probability table) iscontrolled based on information on decoded nodes in the current pointcloud (intra-reference nodes) or information on nodes in a decoded pointcloud (inter-reference nodes).

Buffer 12822 holds, as an intra-reference node (a decoded node), thegenerated information on the decoded node. Buffer 12822 may initializedata held therein, for example for each octree (decoded point cloud).

Buffer 12823 holds information (e.g., the occupancy code) on the decodednode. Buffer 12823 also holds, as a decoded octree, information on thedecoded node on an octree basis. Buffer 12823 may initialize data heldtherein, for example for each octree (decoded point cloud). Point cloudgenerator 12824 transforms the decoded octree into a point cloud togenerate a decoded point cloud.

Buffer 12825 holds the decoded point cloud as an inter-reference pointcloud. Motion compensator 12826 corrects a displacement between aninter-reference point cloud and the point cloud being decoded (motioncompensation) to generate an aligned point cloud, which is theinter-reference point cloud aligned. For example, motion compensator12826 performs motion compensation using motion compensationinformation, such as motion vectors, obtained from the header of theframe or octree or the header of the node information.

Octree generator 12827 transforms the aligned point cloud into an octreerepresentation to generate an inter-reference octree, which representsgeometry information on the aligned point cloud as an octree. Buffer12828 holds the inter-reference octree generated. Buffer 12828 mayinitialize the data held therein, for example for each octree (decodedpoint cloud).

Three-dimensional data decoding device 12820 may perform the motioncompensation on a frame or octree basis, or on a node (point) basis.

The inter-reference point cloud may be a point cloud in a decoded framedifferent from the frame being decoded, or may be a decoded point cloudin the frame being decoded.

Controller 12829 uses intra-reference nodes in buffer 12822 orinter-reference nodes in the inter-reference octree in buffer 12828 tocontrol the probability parameter used by entropy decoder 12821 forentropy decoding (arithmetic decoding) of the current node. Whetherintra reference is used or inter reference is used may be determinedbased on control information in the bitstream, may be predetermined foreach frame or point cloud, or may be determined in any manner, forexample.

For example, if intra reference is used, the probability parameter isselected based on the occupancy state of each of neighboring nodes(intra-reference nodes) of the current node (whether the neighboringnode includes points). If inter reference is used, the probabilityparameter is selected based on the occupancy state of each of nodes(inter-reference nodes) at the same locations in the inter-referenceoctree as at least one of the current node and the neighboring nodes. Ifinter reference is selected, the probability parameter may be controlledby combining inter reference and intra reference.

Three-dimensional data decoding device 12820 may thus control theprobability parameter for entropy decoding based on information oninter-reference nodes in addition to information on intra-referencenodes. In this manner, a point cloud can be decoded from a bitstreamencoded with reference to encoded point clouds (e.g., a bitstream outputby three-dimensional data encoding device 12800 illustrated in FIG. 28).

Three-dimensional data decoding device 12820 does not need to alwaysrefer to the inter-reference point cloud. Rather, three-dimensional datadecoding device 12820 may decode the point cloud being decoded, based ononly information on the point cloud being decoded. This may be done byclearing buffer 12825 storing inter-reference point clouds with the sametiming as the three-dimensional data encoding device, such as atpredetermined time intervals (e.g., every second), at predeterminedframe intervals (e.g., every 30 frames), or at any time when notified bythe three-dimensional data encoding device. In this manner,three-dimensional data decoding device 12820 is allowed to start randomreplay at a point cloud that is located at a position other than thebeginning of the bitstream and that does not refer to anyinter-reference point cloud.

FIG. 30 is a block diagram of three-dimensional data encoding device12800A, which is a variation of three-dimensional data encoding device12800. In addition to the components of three-dimensional data encodingdevice 12800 illustrated in FIG. 28 , three-dimensional data encodingdevice 12800A illustrated in FIG. 30 further includes motion compensator12812.

Motion compensator 12812 performs motion compensation on the encodedpoint cloud generated by point cloud generator 12806, thereby aligningthe encoded point cloud with an inter-reference point cloud stored inbuffer 12807. Buffer 12807 merges the motion-compensated encoded pointcloud with the stored inter-reference point cloud to update the storedinter-reference point cloud. In this manner, a dense point cloudresulting from superposing point clouds of multiple frames on each othercan be used as the inter-reference point cloud. Other processes are thesame as in three-dimensional data encoding device 12800, for example.

The inter-reference point cloud may be a point cloud in an encoded framedifferent from the frame being encoded, or may be an encoded point cloudin the frame being encoded.

Three-dimensional data encoding device 12800A thus aligns and mergesencoded point clouds, which will improve the point cloud density of theinter-reference point cloud. This improves the accuracy of predictingthe probability of the occurrence of information on the current node,and thus will further improve the coding efficiency.

Three-dimensional data encoding device 12800A does not need to refer toall the encoded point clouds as the inter-reference point cloud. Rather,three-dimensional data encoding device 12800A may encode the currentpoint cloud based on information on only the current point cloud orinformation on the current point cloud and some of the encoded pointclouds. This may be done by clearing all or part of the data in buffer12807 storing inter-reference point clouds at predetermined timeintervals (e.g., every second), at predetermined frame intervals (e.g.,every 5 frames), or at any time when notifying the three-dimensionaldata decoding device. Encoding based on information on only the currentpoint cloud enables the three-dimensional data decoding device to startrandom replay at a point cloud that is located at a position other thanthe beginning of the bitstream and that does not refer to anyinter-reference point cloud. This will improve the random accessibilityand the error resistance of the bitstream. Encoding based on informationon the current point cloud and some of the encoded point clouds enablesreducing the capacity of buffer 12807 holding inter-reference pointclouds. This will lead to a reduced implementation cost of thethree-dimensional data encoding device and the three-dimensional datadecoding device.

FIG. 31 is a block diagram of three-dimensional data decoding device12820A, which is a variation of three-dimensional data decoding device12820. In addition to the components of three-dimensional data decodingdevice 12820 illustrated in FIG. 29 , three-dimensional data decodingdevice 12820A illustrated in FIG. 31 further includes motion compensator12830. Three-dimensional data decoding device 12820A decodes a pointcloud from, for example, a bitstream generated by three-dimensional dataencoding device 12800A illustrated in FIG. 30 .

Motion compensator 12830 performs motion compensation on the decodedpoint cloud to align the decoded point cloud with an inter-referencepoint cloud stored in buffer 12825. Buffer 12825 merges themotion-compensated decoded point cloud with the stored inter-referencepoint cloud to update the stored inter-reference point cloud. In thismanner, a dense point cloud resulting from superposing point clouds ofmultiple frames on each other can be used as the inter-reference pointcloud. Other processes are the same as in three-dimensional datadecoding device 12820, for example.

The inter-reference point cloud may be a point cloud in a decoded framedifferent from the frame being decoded, or may be a decoded point cloudin the frame being decoded.

Three-dimensional data decoding device 12820A is thus configured toalign and merge decoded point clouds. Three-dimensional data decodingdevice 12820A can therefore decode a point cloud from a bitstreamencoded by a three-dimensional data encoding device having a similarconfiguration (e.g., a bitstream generated by three-dimensional dataencoding device 12800A illustrated in FIG. 30 ).

Three-dimensional data decoding device 12820A does not need to refer toall the decoded point clouds as the inter-reference point cloud. Rather,three-dimensional data decoding device 12820A may decode the point cloudbeing decoded, based on information on only the point cloud beingdecoded or information on the point cloud being decoded and some of thedecoded point clouds. This may be done by clearing all or part of thedata in buffer 12825 storing inter-reference point clouds atpredetermined time intervals (e.g., every second), at predeterminedframe intervals (e.g., every 5 frames), or at any time when notified bythe three-dimensional data encoding device.

Decoding based on information on only the point cloud being decodedenables three-dimensional data decoding device 12820A to start randomreplay at a point cloud that is located at a position other than thebeginning of the bitstream and that does not refer to anyinter-reference point cloud. This will improve the random accessibilityand the error resistance of the bitstream. Decoding based on informationon the point cloud being decoded and some of the decoded point cloudsenables the three-dimensional data decoding device to have a reducedcapacity of buffer 12825 holding inter-reference point clouds. This willlead to a reduced implementation cost of the three-dimensional dataencoding device and the three-dimensional data decoding device.

FIG. 32 is a diagram illustrating an example of inter prediction in thethree-dimensional data encoding device illustrated in FIG. 28 or 30 .This also applies to inter prediction in the three-dimensional datadecoding device illustrated in FIG. 29 or 31 .

As illustrated in FIG. 32 , the three-dimensional data encoding devicesets, for example, a first cuboid that includes a current point cloud.The three-dimensional data encoding device also sets a second cuboid bytranslating the first cuboid. The second cuboid is a space that includesan encoded point cloud to be referred to in encoding the current pointcloud. The three-dimensional data encoding device may incorporate motionvector information, which is the x, y, and z components of thetranslation distance between the first cuboid and the second cuboid,into the header of the frame or octree, or may incorporate theinformation into the header of the node information afterentropy-encoding the information.

The example here illustrates the use of translation to set the spacethat includes the encoded point cloud to be referred to in encoding thecurrent point cloud. Any method, however, may be employed that canuniquely set the space that includes the encoded point cloud to bereferred to.

Now, an example of header information will be described. FIG. 33 is adiagram illustrating a syntax example of a sequence parameter set (SPS)in a bitstream. The SPS is control information shared by multipleframes, point clouds, or slices, and is control information shared byattribute information and geometry information.

As illustrated in FIG. 33 , the SPS includessps_inter_prediction_enabled_flag and sps_max_num_ref_frames_minus1.

The following describes a semantics example of the sequence parameterset. sps_inter_prediction_enabled_flag equal to 1 specifies that the useof inter prediction is permitted for a bitstream that refers to the SPS.sps_inter_prediction_enabled_flag equal to 0 specifies that interprediction is invalid for a bitstream that refers to the SPS.

sps_max_num_ref_frames_minus1+1 (the value resulting from adding 1 tosps_max_num_ref_frames_minus1) specifies the maximum number of referencepoint cloud frames referred to by a frame. The value ofsps_max_num_ref_frames_minus1 should be within the range from 0 toMaxNumRefFrames-1.

sps_max_num_ref_frames_minus1 is included in the SPS ifsps_inter_prediction_enabled_flag is 1, and not included in the SPS ifsps_inter_prediction_enabled_flag is 0.

FIG. 34 is a diagram illustrating a syntax example of a geometryinformation parameter set (GPS) in a bitstream. The GPS is controlinformation shared by multiple frames, point clouds, or slices, and iscontrol information for geometry information.

As illustrated in FIG. 34 , the GPS includesgps_inter_prediction_enabled_flag and gps_num_ref_frames_minus1.

The following describes a semantics example of the geometry informationparameter set. gps_inter_prediction_enabled_flag equal to 1 specifiesthat the use of inter prediction is permitted when a bitstream thatrefers to the GPS is decoded on a geometry information data basis.gps_inter_prediction_enabled_flag equal to 0 specifies that interprediction is invalid when a bitstream that refers to the GPS is decodedon a geometry information data basis. Ifsps_inter_prediction_enabled_flag is 0,gps_inter_prediction_enabled_flag is 0.

gps_num_ref_frames_minus1+1 (the value resulting from adding 1 togps_num_ref_frames_minus1) specifies the number of reference point cloudframes referred to by a frame that refers to the GPS. The value ofgps_num_ref_frames_minus1 should be within the range from 0 tosps_max_num_ref_frames_minus1.

gps_num_ref_frames_minus1 is included in the GPS ifgps_inter_prediction_enabled_flag is 1, and not included in the GPS ifgps_inter_prediction_enabled_flag is 0.

As illustrated in the above examples, the three-dimensional dataencoding device may notify the three-dimensional data decoding device ofinformation indicating whether inter-prediction encoding is permitted,for example sps_inter_prediction_enabled_flag andgps_inter_prediction_enabled_flag in the sequence parameter set and thegeometry information parameter set. When notifying the three-dimensionaldata decoding device of information indicating that inter-predictionencoding is permitted, the three-dimensional data encoding device mayalso notify the three-dimensional data decoding device of information onthe number of frames referred to in inter-prediction encoding or themaximum value of that number, for example sps_max_num_ref_frames_minus1or gps_num_ref_frames_minus1.

MaxNurmRefFrames is a fixed value specified as a requirement to besatisfied by the three-dimensional data decoding device. This value maybe set to several frames, for example 6 frames, although the value maybe greater than 6 as long as both the three-dimensional data encodingdevice and the three-dimensional data decoding device is configured withthe same value.

The above information may be notified from the three-dimensional dataencoding device to the three-dimensional data decoding device. This willoptimize memory allocation used for processing in the three-dimensionaldata decoding device.

The above information items, that is, the information indicating whetherinter-prediction encoding is permitted and the information on the numberof frames referred to in inter-prediction encoding or the maximum valueof that number, may be stored in both or only one of the SPS and theGPS. These information items may also be stored in control informationother than the SPS and the GPS.

The devices, processes, and syntaxes disclosed with reference to FIGS.28 to 34 may be implemented in combination with at least part of otherembodiments in the present disclosure. Further, some of the devices,processes, and syntaxes disclosed with reference to FIGS. 28 to 34 maybe implemented in combination with other embodiments.

All the components disclosed with reference to FIGS. 28 to 34 are notnecessarily essential. Rather, the devices may include only some of thecomponents.

As described above, the three-dimensional data encoding device accordingto the present embodiment performs the process shown in FIG. 35 . Thethree-dimensional data encoding device performs motion compensation on aplurality of encoded point clouds (S12801). The three-dimensional dataencoding device merges (synthesizes) the plurality of encoded pointclouds that have been motion compensated to generate a reference pointcloud (for example, the inter-reference point cloud in FIG. 30 )(S12802). The three-dimensional data encoding device generates an N-arytree structure (for example, the current octree in FIG. 30 ) of acurrent point cloud, where N is an integer greater than or equal to 2(S12803). The three-dimensional data encoding device encodes the N-arytree structure of the current point cloud using the reference pointcloud (S12804). It should be noted that N is, for example, 8, but may beany exponent of 2, or any other value.

Accordingly, the three-dimensional data encoding device can improveencoding efficiency by encoding a current point cloud using a referencepoint cloud obtained by merging a plurality of encoded point clouds.

For example, in encoding the N-ary tree structure of the current pointcloud (S12804), the three-dimensional data encoding device: performsmotion compensation for the current point cloud on the reference pointcloud; generates an N-ary tree structure (for example, theinter-reference octree in FIG. 30 ) of the reference point cloud thathas been motion compensated; and encodes the N-ary tree structure of thecurrent point cloud using the N-ary tree structure of the referencepoint cloud.

For example, in encoding the N-ary tree structure of the current pointcloud (S12804), the three-dimensional data encoding device entropyencodes the N-ary tree structure of the current point cloud; andcontrols a probability parameter to be used in the entropy encoding,based on the reference point cloud. For example, the three-dimensionaldata encoding device selects the probability parameter to be used from aplurality of probability parameters, based on the reference point cloud.

For example, the three-dimensional data encoding device: generates anencoded current point cloud from the N-ary tree structure of the currentpoint cloud (for example, the encoded point cloud in FIG. 30 ); performsmotion compensation for the reference point cloud on the encoded currentpoint cloud; and merges the encoded current point cloud that has beenmotion compensated with the reference point cloud to update thereference point cloud.

For example, each of the plurality of encoded point clouds belongs to adifferent frame than the current point cloud. For example, each of theplurality of encoded point clouds belongs to a same frame as the currentpoint cloud.

For example, the three-dimensional data encoding device stores, incontrol information (for example, SPS or GPS) which is common to aplurality of point clouds, first information (for example,sps_inter_prediction_enabled_flag or gps_inter_prediction_enabled_flag)indicating whether execution of encoding using the reference point cloudis permitted.

For example, the three-dimensional data encoding device stores, in thecontrol information (for example, SPS or GPS), second information (forexample, sps_max_num_ref_frames_minus1 or gps_num_ref_frames_minus1) ona total number of the plurality of encoded point clouds, when the firstinformation indicates that the execution of the encoding using thereference point cloud is permitted. For example, the second informationindicates the number of encoded point clouds to be merged or a maximumnumber of the encoded point clouds to be merged.

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the above processesusing the memory.

Furthermore, the three-dimensional data decoding device performs theprocess shown in FIG. 36 . The three-dimensional data decoding deviceperforms motion compensation on a plurality of decoded point clouds(S12811). The three-dimensional data decoding device merges(synthesizes) the plurality of decoded point clouds that have beenmotion compensated, to generate a reference point cloud (for example,the inter-reference point cloud in FIG. 31 ) (S12812). Thethree-dimensional data decoding device decodes an N-ary tree structure(for example, the decoded octree in FIG. 31 ) of a current point cloudusing the reference point cloud, where N is an integer greater than orequal to 2 (S12813), Specifically, the three-dimensional data decodingdevice decodes a bitstream (encoded data) generated by encoding theN-ary tree structure of the current point cloud, to obtain the N-arytree structure of the current point cloud. The three-dimensional datadecoding device generates a decoded point cloud of the current pointcloud from the N-ary tree structure of the current point cloud (S12814).It should be noted that N is, for example, 8, but may be any exponent of2, or any other value.

Accordingly, the three-dimensional data decoding device can decode thecurrent point cloud using a reference point cloud obtained by merging aplurality of decoded point clouds.

For example, in decoding the N-ary tree structure of the current pointcloud (S12813), the three-dimensional data decoding device: performsmotion compensation for the current point cloud on the reference pointcloud; generates an N-ary tree structure (for example, theinter-reference octree in FIG. 31 ) of the reference point cloud thathas been motion compensated; and decodes the N-ary tree structure of thecurrent point cloud using the N-ary tree structure of the referencepoint cloud.

For example, in decoding the N-ary tree structure of the current pointcloud (S12813), the three-dimensional data decoding device: entropydecodes the N-ary tree structure of the current point cloud; andcontrols a probability parameter to be used in the entropy decoding,based on the reference point cloud. For example, the three-dimensionaldata encoding device selects the probability parameter to be used from aplurality of probability parameters, based on the reference point cloud.

For example, the three-dimensional data decoding device: performs motioncompensation for the reference point cloud on the decoded point cloud ofthe current point cloud; and merges the decoded point cloud that hasbeen motion compensated with the reference point cloud to update thereference point cloud.

For example, each of the plurality of decoded point clouds belongs to adifferent frame than the current point cloud. For example, each of theplurality of decoded point clouds belongs to a same frame as the currentpoint cloud.

For example, the three-dimensional data decoding device obtains, fromcontrol information (for example, SPS or GPS) which is common to aplurality of point clouds, first information (for example,sps_inter_prediction_enabled_flag or gps_inter_prediction_enabled_flag)indicating whether execution of decoding using the reference point cloudis permitted.

For example, the three-dimensional data decoding device obtains, fromthe control information (for example, SPS or GPS), second information(for example, sps_max_num_ref_frames_minus1 orgps_num_ref_frames_minus1) on a total number of the plurality of decodedpoint clouds, when the first information indicates that the execution ofthe decoding using the reference point cloud is permitted. For example,the second information indicates the number of decoded point clouds tobe merged or a maximum number of the encoded point clouds to be merged.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the above processesusing the memory.

Embodiment 4

The following describes the structure of three-dimensional data creationdevice 810 according to the present embodiment. FIG. 37 is a blockdiagram of an exemplary structure of three-dimensional data creationdevice 810 according to the present embodiment. Such three-dimensionaldata creation device 810 is equipped, for example, in a vehicle.Three-dimensional data creation device 810 transmits and receivesthree-dimensional data to and from an external cloud-based trafficmonitoring system, a preceding vehicle, or a following vehicle, andcreates and stores three-dimensional data.

Three-dimensional data creation device 810 includes data receiver 811,communication unit 812, reception controller 813, format converter 814,a plurality of sensors 815, three-dimensional data creator 816,three-dimensional data synthesizer 817, three-dimensional data storage818, communication unit 819, transmission controller 820, formatconverter 821, and data transmitter 822.

Data receiver 811 receives three-dimensional data 831 from a cloud-basedtraffic monitoring system or a preceding vehicle. Three-dimensional data831 includes, for example, information on a region undetectable bysensors 815 of the own vehicle, such as a point cloud, visible lightvideo, depth information, sensor position information, and speedinformation.

Communication unit 812 communicates with the cloud-based trafficmonitoring system or the preceding vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the preceding vehicle.

Reception controller 813 exchanges information, such as information onsupported formats, with a communications partner via communication unit812 to establish communication with the communications partner.

Format converter 814 applies format conversion, etc. onthree-dimensional data 831 received by data receiver 811 to generatethree-dimensional data 832. Format converter 814 also decompresses ordecodes three-dimensional data 831 when three-dimensional data 831 iscompressed or encoded.

A plurality of sensors 815 are a group of sensors, such as visible lightcameras and infrared cameras, that obtain information on the outside ofthe vehicle and generate sensor information 833. Sensor information 833is, for example, three-dimensional data such as a point cloud (pointgroup data), when sensors 815 are laser sensors such as LiDARs. Notethat a single sensor may serve as a plurality of sensors 815.

Three-dimensional data creator 816 generates three-dimensional data 834from sensor information 833. Three-dimensional data 834 includes, forexample, information such as a point cloud, visible light video, depthinformation, sensor position information, and speed information.

Three-dimensional data synthesizer 817 synthesizes three-dimensionaldata 834 created on the basis of sensor information 833 of the ownvehicle with three-dimensional data 832 created by the cloud-basedtraffic monitoring system or the preceding vehicle, etc., therebyforming three-dimensional data 835 of a space that includes the spaceahead of the preceding vehicle undetectable by sensors 815 of the ownvehicle.

Three-dimensional data storage 818 stores generated three-dimensionaldata 835, etc.

Communication unit 819 communicates with the cloud-based trafficmonitoring system or the following vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the following vehicle.

Transmission controller 820 exchanges information such as information onsupported formats with a communications partner via communication unit819 to establish communication with the communications partner.Transmission controller 820 also determines a transmission region, whichis a space of the three-dimensional data to be transmitted, on the basisof three-dimensional data formation information on three-dimensionaldata 832 generated by three-dimensional data synthesizer 817 and thedata transmission request from the communications partner.

More specifically, transmission controller 820 determines a transmissionregion that includes the space ahead of the own vehicle undetectable bya sensor of the following vehicle, in response to the data transmissionrequest from the cloud-based traffic monitoring system or the followingvehicle. Transmission controller 820 judges, for example, whether aspace is transmittable or whether the already transmitted space includesan update, on the basis of the three-dimensional data formationinformation to determine a transmission region. For example,transmission controller 820 determines, as a transmission region, aregion that is: a region specified by the data transmission request; anda region, corresponding three-dimensional data 835 of which is present.Transmission controller 820 then notifies format converter 821 of theformat supported by the communications partner and the transmissionregion.

Of three-dimensional data 835 stored in three-dimensional data storage818, format converter 821 converts three-dimensional data 836 of thetransmission region into the format supported by the receiver end togenerate three-dimensional data 837. Note that format converter 821 maycompress or encode three-dimensional data 837 to reduce the data amount.

Data transmitter 822 transmits three-dimensional data 837 to thecloud-based traffic monitoring system or the following vehicle. Suchthree-dimensional data 837 includes, for example, information on a blindspot, which is a region hidden from view of the following vehicle, suchas a point cloud ahead of the own vehicle, visible light video, depthinformation, and sensor position information.

Note that an example has been described in which format converter 814and format converter 821 perform format conversion, etc., but formatconversion may not be performed.

With the above structure, three-dimensional data creation device 810obtains, from an external device, three-dimensional data 831 of a regionundetectable by sensors 815 of the own vehicle, and synthesizesthree-dimensional data 831 with three-dimensional data 834 that is basedon sensor information 833 detected by sensors 815 of the own vehicle,thereby generating three-dimensional data 835. Three-dimensional datacreation device 810 is thus capable of generating three-dimensional dataof a range undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 is also capable oftransmitting, to the cloud-based traffic monitoring system or thefollowing vehicle, etc., three-dimensional data of a space that includesthe space ahead of the own vehicle undetectable by a sensor of thefollowing vehicle, in response to the data transmission request from thecloud-based traffic monitoring system or the following vehicle.

The following describes the steps performed by three-dimensional datacreation device 810 of transmitting three-dimensional data to afollowing vehicle. FIG. 38 is a flowchart showing exemplary stepsperformed by three-dimensional data creation device 810 of transmittingthree-dimensional data to a cloud-based traffic monitoring system or afollowing vehicle.

First, three-dimensional data creation device 810 generates and updatesthree-dimensional data 835 of a space that includes space on the roadahead of the own vehicle (S801). More specifically, three-dimensionaldata creation device 810 synthesizes three-dimensional data 834 createdon the basis of sensor information 833 of the own vehicle withthree-dimensional data 831 created by the cloud-based traffic monitoringsystem or the preceding vehicle, etc., for example, thereby formingthree-dimensional data 835 of a space that also includes the space aheadof the preceding vehicle undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 then judges whether anychange has occurred in three-dimensional data 835 of the space includedin the space already transmitted (S802).

When a change has occurred in three-dimensional data 835 of the spaceincluded in the space already transmitted due to, for example, a vehicleor a person entering such space from outside (Yes in S802),three-dimensional data creation device 810 transmits, to the cloud-basedtraffic monitoring system or the following vehicle, thethree-dimensional data that includes three-dimensional data 835 of thespace in which the change has occurred (S803).

Three-dimensional data creation device 810 may transmitthree-dimensional data in which a change has occurred, at the sametiming of transmitting three-dimensional data that is transmitted at apredetermined time interval, or may transmit three-dimensional data inwhich a change has occurred soon after the detection of such change.Stated differently, three-dimensional data creation device 810 mayprioritize the transmission of three-dimensional data of the space inwhich a change has occurred to the transmission of three-dimensionaldata that is transmitted at a predetermined time interval.

Also, three-dimensional data creation device 810 may transmit, asthree-dimensional data of a space in which a change has occurred, thewhole three-dimensional data of the space in which such change hasoccurred, or may transmit only a difference in the three-dimensionaldata (e.g., information on three-dimensional points that have appearedor vanished, or information on the displacement of three-dimensionalpoints).

Three-dimensional data creation device 810 may also transmit, to thefollowing vehicle, meta-data on a risk avoidance behavior of the ownvehicle such as hard breaking warning, before transmittingthree-dimensional data of the space in which a change has occurred. Thisenables the following vehicle to recognize at an early stage that thepreceding vehicle is to perform hard braking, etc., and thus to startperforming a risk avoidance behavior at an early stage such as speedreduction.

When no change has occurred in three-dimensional data 835 of the spaceincluded in the space already transmitted (No in S802), or after stepS803, three-dimensional data creation device 810 transmits, to thecloud-based traffic monitoring system or the following vehicle,three-dimensional data of the space included in the space having apredetermined shape and located ahead of the own vehicle by distance L(S804).

The processes of step S801 through step S804 are repeated, for exampleat a predetermined time interval.

When three-dimensional data 835 of the current space to be transmittedincludes no difference from the three-dimensional map, three-dimensionaldata creation device 810 may not transmit three-dimensional data 837 ofthe space.

In the present embodiment, a client device transmits sensor informationobtained through a sensor to a server or another client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 39 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LiDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 40 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g., transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 1015 are a group of sensors, such as LiDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LiDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LiDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037, Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LiDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 41 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LiDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g., transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when received sensor information 1037is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LiDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 42is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 43 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 44 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 45 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

The following describes variations of the present embodiment.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LiDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 46 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 47 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmitsobtained sensor information 1033 to server 901 or another client device902.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1033 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another clientdevice 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using received sensor information1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses received sensor information 1037, andcreates three-dimensional data 1134 using sensor information 1132 thathas been decoded or decompressed. This enables server 901 to reduce theamount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

The following will describe a variation of the present embodiment. FIG.48 is a diagram illustrating a configuration of a system according tothe present embodiment. The system illustrated in FIG. 48 includesserver 2001, client device 2002A, and client device 2002B.

Client device 2002A and client device 2002B are each provided in amobile object such as a vehicle, and transmit sensor information toserver 2001. Server 2001 transmits a three-dimensional map (a pointcloud) to client device 2002A and client device 2002B.

Client device 2002A includes sensor information obtainer 2011, storage2012, and data transmission possibility determiner 2013. It should benoted that client device 2002B has the same configuration. Additionally,when client device 2002A and client device 2002B are not particularlydistinguished below, client device 2002A and client device 2002B arealso referred to as client device 2002.

FIG. 49 is a flowchart illustrating operation of client device 2002according to the present embodiment.

Sensor information obtainer 2011 obtains a variety of sensor informationusing sensors (a group of sensors) provided in a mobile object. In otherwords, sensor information obtainer 2011 obtains sensor informationobtained by the sensors (the group of sensors) provided in the mobileobject and indicating a surrounding state of the mobile object. Sensorinformation obtainer 2011 also stores the obtained sensor informationinto storage 2012. This sensor information includes at least one ofinformation obtained by LiDAR, a visible light image, an infrared image,or a depth image. Additionally, the sensor information may include atleast one of sensor position information, speed information, obtainmenttime information, or obtainment location information. Sensor positioninformation indicates a position of a sensor that has obtained sensorinformation. Speed information indicates a speed of the mobile objectwhen a sensor obtained sensor information. Obtainment time informationindicates a time when a sensor obtained sensor information. Obtainmentlocation information indicates a position of the mobile object or asensor when the sensor obtained sensor information.

Next, data transmission possibility determiner 2013 determines whetherthe mobile object (client device 2002) is in an environment in which themobile object can transmit sensor information to server 2001 (S2002).For example, data transmission possibility determiner 2013 may specify alocation and a time at which client device 2002 is present using GPSinformation etc., and may determine whether data can be transmitted.Additionally, data transmission possibility determiner 2013 maydetermine whether data can be transmitted, depending on whether it ispossible to connect to a specific access point.

When client device 2002 determines that the mobile object is in theenvironment in which the mobile object can transmit the sensorinformation to server 2001 (YES in S2002), client device 2002 transmitsthe sensor information to server 2001 (S2003). In other words, whenclient device 2002 becomes capable of transmitting sensor information toserver 2001, client device 2002 transmits the sensor information held byclient device 2002 to server 2001. For example, an access point thatenables high-speed communication using millimeter waves is provided inan intersection or the like. When client device 2002 enters theintersection, client device 2002 transmits the sensor information heldby client device 2002 to server 2001 at high speed using themillimeter-wave communication.

Next, client device 2002 deletes from storage 2012 the sensorinformation that has been transmitted to server 2001 (S2004). It shouldbe noted that when sensor information that has not been transmitted toserver 2001 meets predetermined conditions, client device 2002 maydelete the sensor information. For example, when an obtainment time ofsensor information held by client device 2002 precedes a current time bya certain time, client device 2002 may delete the sensor informationfrom storage 2012. In other words, when a difference between the currenttime and a time when a sensor obtained sensor information exceeds apredetermined time, client device 2002 may delete the sensor informationfrom storage 2012. Besides, when an obtainment location of sensorinformation held by client device 2002 is separated from a currentlocation by a certain distance, client device 2002 may delete the sensorinformation from storage 2012. In other words, when a difference betweena current position of the mobile object or a sensor and a position ofthe mobile object or the sensor when the sensor obtained sensorinformation exceeds a predetermined distance, client device 2002 maydelete the sensor information from storage 2012. Accordingly, it ispossible to reduce the capacity of storage 2012 of client device 2002.

When client device 2002 does not finish obtaining sensor information (NOin S2005), client device 2002 performs step S2001 and the subsequentsteps again. Further, when client device 2002 finishes obtaining sensorinformation (YES in S2005), client device 2002 completes the process.

Client device 2002 may select sensor information to be transmitted toserver 2001, in accordance with communication conditions. For example,when high-speed communication is available, client device 2002preferentially transmits sensor information (e.g., information obtainedby LiDAR) of which the data size held in storage 2012 is large.Additionally, when high-speed communication is not readily available,client device 2002 transmits sensor information (e.g., a visible lightimage) which has high priority and of which the data size held instorage 2012 is small. Accordingly, client device 2002 can efficientlytransmit sensor information held in storage 2012, in accordance withnetwork conditions

Client device 2002 may obtain, from server 2001, time informationindicating a current time and location information indicating a currentlocation. Moreover, client device 2002 may determine an obtainment timeand an obtainment location of sensor information based on the obtainedtime information and location information. In other words, client device2002 may obtain time information from server 2001 and generateobtainment time information using the obtained time information. Clientdevice 2002 may also obtain location information from server 2001 andgenerate obtainment location information using the obtained locationinformation.

For example, regarding time information, server 2001 and client device2002 perform clock synchronization using a means such as the NetworkTime Protocol (NTP) or the Precision Time Protocol (PTP). This enablesclient device 2002 to obtain accurate time information. What's more,since it is possible to synchronize clocks between server 2001 andclient devices 2002, it is possible to synchronize times included inpieces of sensor information obtained by separate client devices 2002.As a result, server 2001 can handle sensor information indicating asynchronized time. It should be noted that a means of synchronizingclocks may be any means other than the NTP or PTP. In addition, GPSinformation may be used as the time information and the locationinformation.

Server 2001 may specify a time or a location and obtain pieces of sensorinformation from client devices 2002. For example, when an accidentoccurs, in order to search for a client device in the vicinity of theaccident, server 2001 specifies an accident occurrence time and anaccident occurrence location and broadcasts sensor informationtransmission requests to client devices 2002. Then, client device 2002having sensor information obtained at the corresponding time andlocation transmits the sensor information to server 2001. In otherwords, client device 2002 receives, from server 2001, a sensorinformation transmission request including specification informationspecifying a location and a time. When sensor information obtained at alocation and a time indicated by the specification information is storedin storage 2012, and client device 2002 determines that the mobileobject is present in the environment in which the mobile object cantransmit the sensor information to server 2001, client device 2002transmits, to server 2001, the sensor information obtained at thelocation and the time indicated by the specification information.Consequently, server 2001 can obtain the pieces of sensor informationpertaining to the occurrence of the accident from client devices 2002,and use the pieces of sensor information for accident analysis etc.

It should be noted that when client device 2002 receives a sensorinformation transmission request from server 2001, client device 2002may refuse to transmit sensor information. Additionally, client device2002 may set in advance which pieces of sensor information can betransmitted. Alternatively, server 2001 may inquire of client device2002 each time whether sensor information can be transmitted.

A point may be given to client device 2002 that has transmitted sensorinformation to server 2001. This point can be used in payment for, forexample, gasoline expenses, electric vehicle (EV) charging expenses, ahighway toll, or rental car expenses. After obtaining sensorinformation, server 2001 may delete information for specifying clientdevice 2002 that has transmitted the sensor information. For example,this information is a network address of client device 2002. Since thisenables the anonymization of sensor information, a user of client device2002 can securely transmit sensor information from client device 2002 toserver 2001. Server 2001 may include servers. For example, by serverssharing sensor information, even when one of the servers breaks down,the other servers can communicate with client device 2002. Accordingly,it is possible to avoid service outage due to a server breakdown.

A specified location specified by a sensor information transmissionrequest indicates an accident occurrence location etc., and may bedifferent from a position of client device 2002 at a specified timespecified by the sensor information transmission request. For thisreason, for example, by specifying, as a specified location, a rangesuch as within XX meters of a surrounding area, server 2001 can requestinformation from client device 2002 within the range. Similarly, server2001 may also specify, as a specified time, a range such as within Nseconds before and after a certain time. As a result, server 2001 canobtain sensor information from client device 2002 present for a timefrom t-N to t+N and in a location within XX meters from absoluteposition S. When client device 2002 transmits three-dimensional datasuch as LiDAR, client device 2002 may transmit data created immediatelyafter time t.

Server 2001 may separately specify information indicating, as aspecified location, a location of client device 2002 from which sensorinformation is to be obtained, and a location at which sensorinformation is desirably obtained. For example, server 2001 specifiesthat sensor information including at least a range within YY meters fromabsolute position S is to be obtained from client device 2002 presentwithin XX meters from absolute position S. When client device 2002selects three-dimensional data to be transmitted, client device 2002selects one or more pieces of three-dimensional data so that the one ormore pieces of three-dimensional data include at least the sensorinformation including the specified range. Each of the one or morepieces of three-dimensional data is a random-accessible unit of data. Inaddition, when client device 2002 transmits a visible light image,client device 2002 may transmit pieces of temporally continuous imagedata including at least a frame immediately before or immediately aftertime t.

When client device 2002 can use physical networks such as 5G, Wi-Fi, ormodes in 5G for transmitting sensor information, client device 2002 mayselect a network to be used according to the order of priority notifiedby server 2001. Alternatively, client device 2002 may select a networkthat enables client device 2002 to ensure an appropriate bandwidth basedon the size of transmit data. Alternatively, client device 2002 mayselect a network to be used, based on data transmission expenses etc. Atransmission request from server 2001 may include information indicatinga transmission deadline, for example, performing transmission whenclient device 2002 can start transmission by time t. When server 2001cannot obtain sufficient sensor information within a time limit, server2001 may issue a transmission request again.

Sensor information may include header information indicatingcharacteristics of sensor data along with compressed or uncompressedsensor data. Client device 2002 may transmit header information toserver 2001 via a physical network or a communication protocol that isdifferent from a physical network or a communication protocol used forsensor data. For example, client device 2002 transmits headerinformation to server 2001 prior to transmitting sensor data. Server2001 determines whether to obtain the sensor data of client device 2002,based on a result of analysis of the header information. For example,header information may include information indicating a point cloudobtainment density, an elevation angle, or a frame rate of LiDAR, orinformation indicating, for example, a resolution, an SN ratio, or aframe rate of a visible light image. Accordingly, server 2001 can obtainthe sensor information from client device 2002 having the sensor data ofdetermined quality.

As stated above, client device 2002 is provided in the mobile object,obtains sensor information that has been obtained by a sensor providedin the mobile object and indicates a surrounding state of the mobileobject, and stores the sensor information into storage 2012. Clientdevice 2002 determines whether the mobile object is present in anenvironment in which the mobile object is capable of transmitting thesensor information to server 2001, and transmits the sensor informationto server 2001 when the mobile object is determined to be present in theenvironment in which the mobile object is capable of transmitting thesensor information to server 2001.

Additionally, client device 2002 further creates, from the sensorinformation, three-dimensional data of a surrounding area of the mobileobject, and estimates a self-location of the mobile object using thethree-dimensional data created.

Besides, client device 2002 further transmits a transmission request fora three-dimensional map to server 2001, and receives thethree-dimensional map from server 2001. In the estimating, client device2002 estimates the self-location using the three-dimensional data andthe three-dimensional map.

It should be noted that the above process performed by client device2002 may be realized as an information transmission method for use inclient device 2002.

In addition, client device 2002 may include a processor and memory.Using the memory, the processor may perform the above process.

Next, a sensor information collection system according to the presentembodiment will be described. FIG. 50 is a diagram illustrating aconfiguration of the sensor information collection system according tothe present embodiment. As illustrated in FIG. 50 , the sensorinformation collection system according to the present embodimentincludes terminal 2021A, terminal 2021B, communication device 2022A,communication device 2022B, network 2023, data collection server 2024,map server 2025, and client device 2026. It should be noted that whenterminal 2021A and terminal 2021B are not particularly distinguished,terminal 2021A and terminal 2021B are also referred to as terminal 2021.Additionally, when communication device 2022A and communication device2022B are not particularly distinguished, communication device 2022A andcommunication device 2022B are also referred to as communication device2022.

Data collection server 2024 collects data such as sensor data obtainedby a sensor included in terminal 2021 as position-related data in whichthe data is associated with a position in a three-dimensional space.

Sensor data is data obtained by, for example, detecting a surroundingstate of terminal 2021 or an internal state of terminal 2021 using asensor included in terminal 2021. Terminal 2021 transmits, to datacollection server 2024, one or more pieces of sensor data collected fromone or more sensor devices in locations at which direct communicationwith terminal 2021 is possible or at which communication with terminal2021 is possible by the same communication system or via one or morerelay devices.

Data included in position-related data may include, for example,information indicating an operating state, an operating log, a serviceuse state, etc. of a terminal or a device included in the terminal. Inaddition, the data include in the position-related data may include, forexample, information in which an identifier of terminal 2021 isassociated with a position or a movement path etc. of terminal 2021.

Information indicating a position included in position-related data isassociated with, for example, information indicating a position inthree-dimensional data such as three-dimensional map data. The detailsof information indicating a position will be described later.

Position-related data may include at least one of the above-describedtime information or information indicating an attribute of data includedin the position-related data or a type (e.g., a model number) of asensor that has created the data, in addition to position informationthat is information indicating a position. The position information andthe time information may be stored in a header area of theposition-related data or a header area of a frame that stores theposition-related data. Further, the position information and the timeinformation may be transmitted and/or stored as metadata associated withthe position-related data, separately from the position-related data.

Map server 2025 is connected to, for example, network 2023, andtransmits three-dimensional data such as three-dimensional map data inresponse to a request from another device such as terminal 2021.Besides, as described in the aforementioned embodiments, map server 2025may have, for example, a function of updating three-dimensional datausing sensor information transmitted from terminal 2021.

Data collection server 2024 is connected to, for example, network 2023,collects position-related data from another device such as terminal2021, and stores the collected position-related data into a storage ofdata collection server 2024 or a storage of another server. In addition,data collection server 2024 transmits, for example, metadata ofcollected position-related data or three-dimensional data generatedbased on the position-related data, to terminal 2021 in response to arequest from terminal 2021.

Network 2023 is, for example, a communication network such as theInternet. Terminal 2021 is connected to network 2023 via communicationdevice 2022. Communication device 2022 communicates with terminal 2021using one communication system or switching between communicationsystems. Communication device 2022 is a communication satellite thatperforms communication using, for example, (1) a base station compliantwith Long-Term Evolution (LTE) etc., (2) an access point (AP) for Wi-Fior millimeter-wave communication etc., (3) a low-power wide-area (LPWA)network gateway such as SIGFOX, LoRaWAN, or Wi-SUN, or (4) a satellitecommunication system such as DVB-S2.

It should be noted that a base station may communicate with terminal2021 using a system classified as an LPWA network such as NarrowbandInternet of Things (NB IoT) or LTE-M, or switching between thesesystems.

Here, although, in the example given, terminal 2021 has a function ofcommunicating with communication device 2022 that uses two types ofcommunication systems, and communicates with map server 2025 or datacollection server 2024 using one of the communication systems orswitching between the communication systems and between communicationdevices 2022 to be a direct communication partner; a configuration ofthe sensor information collection system and terminal 2021 is notlimited to this. For example, terminal 2021 need not have a function ofperforming communication using communication systems, and may have afunction of performing communication using one of the communicationsystems. Terminal 2021 may also support three or more communicationsystems. Additionally, each terminal 2021 may support a differentcommunication system.

Terminal 2021 includes, for example, the configuration of client device902 illustrated in FIG. 40 . Terminal 2021 estimates a self-locationetc. using received three-dimensional data. Besides, terminal 2021associates sensor data obtained from a sensor and position informationobtained by self-location estimation to generate position-related data.

Position information appended to position-related data indicates, forexample, a position in a coordinate system used for three-dimensionaldata. For example, the position information is coordinate valuesrepresented using a value of a latitude and a value of a longitude.Here, terminal 2021 may include, in the position information, acoordinate system serving as a reference for the coordinate values andinformation indicating three-dimensional data used for locationestimation, along with the coordinate values. Coordinate values may alsoinclude altitude information.

The position information may be associated with a data unit or a spaceunit usable for encoding the above three-dimensional data. Such a unitis, for example, WLD, GOS, SPC, VLM, or VXL, Here, the positioninformation is represented by, for example, an identifier foridentifying a data unit such as the SPC corresponding toposition-related data. It should be noted that the position informationmay include, for example, information indicating three-dimensional dataobtained by encoding a three-dimensional space including a data unitsuch as the SPC or information indicating a detailed position within theSPC, in addition to the identifier for identifying the data unit such asthe SPC. The information indicating the three-dimensional data is, forexample, a file name of the three-dimensional data.

As stated above, by generating position-related data associated withposition information based on location estimation usingthree-dimensional data, the system can give more accurate positioninformation to sensor information than when the system appends positioninformation based on a self-location of a client device (terminal 2021)obtained using a GPS to sensor information. As a result, even whenanother device uses the position-related data in another service, thereis a possibility of more accurately determining a position correspondingto the position-related data in an actual space, by performing locationestimation based on the same three-dimensional data.

It should be noted that although the data transmitted from terminal 2021is the position-related data in the example given in the presentembodiment, the data transmitted from terminal 2021 may be dataunassociated with position information. In other words, the transmissionand reception of three-dimensional data or sensor data described in theother embodiments may be performed via network 2023 described in thepresent embodiment.

Next, a different example of position information indicating a positionin a three-dimensional or two-dimensional actual space or in a map spacewill be described. The position information appended to position-relateddata may be information indicating a relative position relative to akeypoint in three-dimensional data. Here, the keypoint serving as areference for the position information is encoded as, for example, SWLD,and notified to terminal 2021 as three-dimensional data.

The information indicating the relative position relative to thekeypoint may be, for example, information that is represented by avector from the keypoint to the point indicated by the positioninformation, and indicates a direction and a distance from the keypointto the point indicated by the position information. Alternatively, theinformation indicating the relative position relative to the keypointmay be information indicating an amount of displacement from thekeypoint to the point indicated by the position information along eachof the x axis, the y axis, and the z axis. Additionally, the informationindicating the relative position relative to the keypoint may beinformation indicating a distance from each of three or more keypointsto the point indicated by the position information. It should be notedthat the relative position need not be a relative position of the pointindicated by the position information represented using each keypoint asa reference, and may be a relative position of each keypoint representedwith respect to the point indicated by the position information.Examples of position information based on a relative position relativeto a keypoint include information for identifying a keypoint to be areference, and information indicating the relative position of the pointindicated by the position information and relative to the keypoint. Whenthe information indicating the relative position relative to thekeypoint is provided separately from three-dimensional data, theinformation indicating the relative position relative to the keypointmay include, for example, coordinate axes used in deriving the relativeposition, information indicating a type of the three-dimensional data,and/or information indicating a magnitude per unit amount (e.g., ascale) of a value of the information indicating the relative position.

The position information may include, for each keypoint, informationindicating a relative position relative to the keypoint. When theposition information is represented by relative positions relative tokeypoints, terminal 2021 that intends to identify a position in anactual space indicated by the position information may calculatecandidate points of the position indicated by the position informationfrom positions of the keypoints each estimated from sensor data, and maydetermine that a point obtained by averaging the calculated candidatepoints is the point indicated by the position information. Since thisconfiguration reduces the effects of errors when the positions of thekeypoints are estimated from the sensor data, it is possible to improvethe estimation accuracy for the point in the actual space indicated bythe position information. Besides, when the position informationincludes information indicating relative positions relative tokeypoints, if it is possible to detect any one of the keypointsregardless of the presence of keypoints undetectable due to a limitationsuch as a type or performance of a sensor included in terminal 2021, itis possible to estimate a value of the point indicated by the positioninformation.

A point identifiable from sensor data can be used as a keypoint.Examples of the point identifiable from the sensor data include a pointor a point within a region that satisfies a predetermined keypointdetection condition, such as the above-described three-dimensionalfeature or feature of visible light data is greater than or equal to athreshold value.

Moreover, a marker etc. placed in an actual space may be used as akeypoint. In this case, the maker may be detected and located from dataobtained using a sensor such as LiDAR or a camera. For example, themarker may be represented by a change in color or luminance value(degree of reflection), or a three-dimensional shape (e.g., unevenness).Coordinate values indicating a position of the marker, or atwo-dimensional bar code or a bar code etc. generated from an identifierof the marker may be also used.

Furthermore, a light source that transmits an optical signal may be usedas a marker. When a light source of an optical signal is used as amarker, not only information for obtaining a position such as coordinatevalues or an identifier but also other data may be transmitted using anoptical signal. For example, an optical signal may include contents ofservice corresponding to the position of the marker, an address forobtaining contents such as a URL, or an identifier of a wirelesscommunication device for receiving service, and information indicating awireless communication system etc. for connecting to the wirelesscommunication device. The use of an optical communication device (alight source) as a marker not only facilitates the transmission of dataother than information indicating a position but also makes it possibleto dynamically change the data.

Terminal 2021 finds out a correspondence relationship of keypointsbetween mutually different data using, for example, a common identifierused for the data, or information or a table indicating thecorrespondence relationship of the keypoints between the data. Whenthere is no information indicating a correspondence relationship betweenkeypoints, terminal 2021 may also determine that when coordinates of akeypoint in three-dimensional data are converted into a position in aspace of another three-dimensional data, a keypoint closest to theposition is a corresponding keypoint.

When the position information based on the relative position describedabove is used, terminal 2021 that uses mutually differentthree-dimensional data or services can identify or estimate a positionindicated by the position information with respect to a common keypointincluded in or associated with each three-dimensional data. As a result,terminal 2021 that uses the mutually different three-dimensional data orthe services can identify or estimate the same position with higheraccuracy.

Even when map data or three-dimensional data represented using mutuallydifferent coordinate systems are used, since it is possible to reducethe effects of errors caused by the conversion of a coordinate system,it is possible to coordinate services based on more accurate positioninformation.

Hereinafter, an example of functions provided by data collection server2024 will be described. Data collection server 2024 may transferreceived position-related data to another data server. When there aredata servers, data collection server 2024 determines to which dataserver received position-related data is to be transferred, andtransfers the position-related data to a data server determined as atransfer destination.

Data collection server 2024 determines a transfer destination based on,for example, transfer destination server determination rules preset todata collection server 2024. The transfer destination serverdetermination rules are set by, for example, a transfer destinationtable in which identifiers respectively associated with terminals 2021are associated with transfer destination data servers.

Terminal 2021 appends an identifier associated with terminal 2021 toposition-related data to be transmitted, and transmits theposition-related data to data collection server 2024. Data collectionserver 2024 determines a transfer destination data server correspondingto the identifier appended to the position-related data, based on thetransfer destination server determination rules set out using thetransfer destination table etc.; and transmits the position-related datato the determined data server. The transfer destination serverdetermination rules may be specified based on a determination conditionset using a time, a place, etc. at which position-related data isobtained. Here, examples of the identifier associated with transmissionsource terminal 2021 include an identifier unique to each terminal 2021or an identifier indicating a group to which terminal 2021 belongs.

The transfer destination table need not be a table in which identifiersassociated with transmission source terminals are directly associatedwith transfer destination data servers. For example, data collectionserver 2024 holds a management table that stores tag informationassigned to each identifier unique to terminal 2021, and a transferdestination table in which the pieces of tag information are associatedwith transfer destination data servers. Data collection server 2024 maydetermine a transfer destination data server based on tag information,using the management table and the transfer destination table. Here, thetag information is, for example, control information for management orcontrol information for providing service assigned to a type, a modelnumber, an owner of terminal 2021 corresponding to the identifier, agroup to which terminal 2021 belongs, or another identifier. Moreover,in the transfer destination able, identifiers unique to respectivesensors may be used instead of the identifiers associated withtransmission source terminals 2021. Furthermore, the transferdestination server determination rules may be set by client device 2026.

Data collection server 2024 may determine data servers as transferdestinations, and transfer received position-related data to the dataservers. According to this configuration, for example, whenposition-related data is automatically backed up or when, in order thatposition-related data is commonly used by different services, there is aneed to transmit the position-related data to a data server forproviding each service, it is possible to achieve data transfer asintended by changing a setting of data collection server 2024. As aresult, it is possible to reduce the number of steps necessary forbuilding and changing a system, compared to when a transmissiondestination of position-related data is set for each terminal 2021.

Data collection server 2024 may register, as a new transfer destination,a data server specified by a transfer request signal received from adata server; and transmit position-related data subsequently received tothe data server, in response to the transfer request signal.

Data collection server 2024 may store position-related data receivedfrom terminal 2021 into a recording device, and transmitposition-related data specified by a transmission request signalreceived from terminal 2021 or a data server to request source terminal2021 or the data server in response to the transmission request signal.

Data collection server 2024 may determine whether position-related datais suppliable to a request source data server or terminal 2021, andtransfer or transmit the position-related data to the request sourcedata server or terminal 2021 when determining that the position-relateddata is suppliable.

When data collection server 2024 receives a request for currentposition-related data from client device 2026, even if it is not atiming for transmitting position-related data by terminal 2021, datacollection server 2024 may send a transmission request for the currentposition-related data to terminal 2021, and terminal 2021 may transmitthe current position-related data in response to the transmissionrequest.

Although terminal 2021 transmits position information data to datacollection server 2024 in the above description, data collection server2024 may have a function of managing terminal 2021 such as a functionnecessary for collecting position-related data from terminal 2021 or afunction used when collecting position-related data from terminal 2021.

Data collection server 2024 may have a function of transmitting, toterminal 2021, a data request signal for requesting transmission ofposition information data, and collecting position-related data.

Management information such as an address for communicating withterminal 2021 from which data is to be collected or an identifier uniqueto terminal 2021 is registered in advance in data collection server2024. Data collection server 2024 collects position-related data fromterminal 2021 based on the registered management information. Managementinformation may include information such as types of sensors included interminal 2021, the number of sensors included in terminal 2021, andcommunication systems supported by terminal 2021.

Data collection server 2024 may collect information such as an operatingstate or a current position of terminal 2021 from terminal 2021.

Registration of management information may be instructed by clientdevice 2026, or a process for the registration may be started byterminal 2021 transmitting a registration request to data collectionserver 2024. Data collection server 2024 may have a function ofcontrolling communication between data collection server 2024 andterminal 2021.

Communication between data collection server 2024 and terminal 2021 maybe established using a dedicated line provided by a service providersuch as a mobile network operator (MNO) or a mobile virtual networkoperator (MVNO), or a virtual dedicated line based on a virtual privatenetwork (VPN). According to this configuration, it is possible toperform secure communication between terminal 2021 and data collectionserver 2024.

Data collection server 2024 may have a function of authenticatingterminal 2021 or a function of encrypting data to be transmitted andreceived between data collection server 2024 and terminal 2021. Here,the authentication of terminal 2021 or the encryption of data isperformed using, for example, an identifier unique to terminal 2021 oran identifier unique to a terminal group including terminals 2021, whichis shared in advance between data collection server 2024 and terminal2021. Examples of the identifier include an international mobilesubscriber identity (IMSI) that is a unique number stored in asubscriber identity module (SIM) card. An identifier for use inauthentication and an identifier for use in encryption of data may beidentical or different.

The authentication or the encryption of data between data collectionserver 2024 and terminal 2021 is feasible when both data collectionserver 2024 and terminal 2021 have a function of performing the process.The process does not depend on a communication system used bycommunication device 2022 that performs relay. Accordingly, since it ispossible to perform the common authentication or encryption withoutconsidering whether terminal 2021 uses a communication system, theuser's convenience of system architecture is increased. However, theexpression “does not depend on a communication system used bycommunication device 2022 that performs relay” means a change accordingto a communication system is not essential. In other words, in order toimprove the transfer efficiency or ensure security, the authenticationor the encryption of data between data collection server 2024 andterminal 2021 may be changed according to a communication system used bya relay device.

Data collection server 2024 may provide client device 2026 with a UserInterface (UI) that manages data collection rules such as types ofposition-related data collected from terminal 2021 and data collectionschedules. Accordingly, a user can specify, for example, terminal 2021from which data is to be collected using client device 2026, a datacollection time, and a data collection frequency. Additionally, datacollection server 2024 may specify, for example, a region on a map fromwhich data is to be desirably collected, and collect position-relateddata from terminal 2021 included in the region.

When the data collection rules are managed on a per terminal 2021 basis,client device 2026 presents, on a screen, a list of terminals 2021 orsensors to be managed. The user sets, for example, a necessity for datacollection or a collection schedule for each item in the list.

When a region on a map from which data is to be desirably collected isspecified, client device 2026 presents, on a screen, a two-dimensionalor three-dimensional map of a region to be managed. The user selects theregion from which data is to be collected on the displayed map. Examplesof the region selected on the map include a circular or rectangularregion having a point specified on the map as the center, or a circularor rectangular region specifiable by a drag operation. Client device2026 may also select a region in a preset unit such as a city, an areaor a block in a city, or a main road, etc. Instead of specifying aregion using a map, a region may be set by inputting values of alatitude and a longitude, or a region may be selected from a list ofcandidate regions derived based on inputted text information. Textinformation is, for example, a name of a region, a city, or a landmark.

Moreover, data may be collected while the user dynamically changes aspecified region by specifying one or more terminals 2021 and setting acondition such as within 100 meters of one or more terminals 2021.

When client device 2026 includes a sensor such as a camera, a region ona map may be specified based on a position of client device 2026 in anactual space obtained from sensor data. For example, client device 2026may estimate a self-location using sensor data, and specify, as a regionfrom which data is to be collected, a region within a predetermineddistance from a point on a map corresponding to the estimated locationor a region within a distance specified by the user. Client device 2026may also specify, as the region from which the data is to be collected,a sensing region of the sensor, that is, a region corresponding toobtained sensor data. Alternatively, client device 2026 may specify, asthe region from which the data is to be collected, a region based on alocation corresponding to sensor data specified by the user. Eitherclient device 2026 or data collection server 2024 may estimate a regionon a map or a location corresponding to sensor data.

When a region on a map is specified, data collection server 2024 mayspecify terminal 2021 within the specified region by collecting currentposition information of each terminal 2021, and may send a transmissionrequest for position-related data to specified terminal 2021. When datacollection server 2024 transmits information indicating a specifiedregion to terminal 2021, determines whether terminal 2021 is presentwithin the specified region, and determines that terminal 2021 ispresent within the specified region, rather than specifying terminal2021 within the region, terminal 2021 may transmit position-relateddata.

Data collection server 2024 transmits, to client device 2026, data suchas a list or a map for providing the above-described User Interface (UI)in an application executed by client device 2026. Data collection server2024 may transmit, to client device 2026, not only the data such as thelist or the map but also an application program. Additionally, the aboveUI may be provided as contents created using HTML displayable by abrowser. It should be noted that part of data such as map data may besupplied from a server, such as map server 2025, other than datacollection server 2024.

When client device 2026 receives an input for notifying the completionof an input such as pressing of a setup key by the user, client device2026 transmits the inputted information as configuration information todata collection server 2024. Data collection server 2024 transmits, toeach terminal 2021, a signal for requesting position-related data ornotifying position-related data collection rules, based on theconfiguration information received from client device 2026, and collectsthe position-related data.

Next, an example of controlling operation of terminal 2021 based onadditional information added to three-dimensional or two-dimensional mapdata will be described.

In the present configuration, object information that indicates aposition of a power feeding part such as a feeder antenna or a feedercoil for wireless power feeding buried under a road or a parking lot isincluded in or associated with three-dimensional data, and such objectinformation is provided to terminal 2021 that is a vehicle or a drone.

A vehicle or a drone that has obtained the object information to getcharged automatically moves so that a position of a charging part suchas a charging antenna or a charging coil included in the vehicle or thedrone becomes opposite to a region indicated by the object information,and such vehicle or a drone starts to charge itself. It should be notedthat when a vehicle or a drone has no automatic driving function, adirection to move in or an operation to perform is presented to a driveror an operator by using an image displayed on a screen, audio, etc. Whena position of a charging part calculated based on an estimatedself-location is determined to fall within the region indicated by theobject information or a predetermined distance from the region, an imageor audio to be presented is changed to a content that puts a stop todriving or operating, and the charging is started.

Object information need not be information indicating a position of apower feeding part, and may be information indicating a region withinwhich placement of a charging part results in a charging efficiencygreater than or equal to a predetermined threshold value. A positionindicated by object information may be represented by, for example, thecentral point of a region indicated by the object information, a regionor a line within a two-dimensional plane, or a region, a line, or aplane within a three-dimensional space.

According to this configuration, since it is possible to identify theposition of the power feeding antenna unidentifiable by sensing data ofLiDAR or an image captured by the camera, it is possible to highlyaccurately align a wireless charging antenna included in terminal 2021such as a vehicle with a wireless power feeding antenna buried under aroad. As a result, it is possible to increase a charging speed at thetime of wireless charging and improve the charging efficiency.

Object information may be an object other than a power feeding antenna.For example, three-dimensional data includes, for example, a position ofan AP for millimeter-wave wireless communication as object information.Accordingly, since terminal 2021 can identify the position of the AP inadvance, terminal 2021 can steer a directivity of beam to a direction ofthe object information and start communication. As a result, it ispossible to improve communication quality such as increasingtransmission rates, reducing the duration of time before startingcommunication, and extending a communicable period.

Object information may include information indicating a type of anobject corresponding to the object information. In addition, whenterminal 2021 is present within a region in an actual spacecorresponding to a position in three-dimensional data of the objectinformation or within a predetermined distance from the region, theobject information may include information indicating a process to beperformed by terminal 2021.

Object information may be provided by a server different from a serverthat provides three-dimensional data. When object information isprovided separately from three-dimensional data, object groups in whichobject information used by the same service is stored may be eachprovided as separate data according to a type of a target service or atarget device.

Three-dimensional data used in combination with object information maybe point cloud data of WLD or keypoint data of SWLD.

In the three-dimensional data encoding device, when attributeinformation of a current three-dimensional point to be encoded islayer-encoded using Levels of Detail (LoDs), the three-dimensional datadecoding device may decode the attribute information in layers down toLoD required by the three-dimensional data decoding device and need notdecode the attribute information in layers not required. For example,when the total number of LoDs for the attribute information in abitstream generated by the three-dimensional data encoding device is N,the three-dimensional data decoding device may decode M LoDs (M<N),i.e., layers from the uppermost layer LoD0 to LoD(M-1), and need notdecode the remaining LoDs, i.e., layers down to LoD(N-1). With this,while reducing the processing load, the three-dimensional data decodingdevice can decode the attribute information in layers from LoD0 toLoD(M-1) required by the three-dimensional data decoding device.

FIG. 51 is a diagram illustrating the foregoing use case. In the exampleshown in FIG. 51 , a server stores a three-dimensional map obtained byencoding three-dimensional geometry information and attributeinformation. The server (the three-dimensional data encoding device)broadcasts the three-dimensional map to client devices (thethree-dimensional data decoding devices: for example, vehicles, drones,etc.) in an area managed by the server, and each client device uses thethree-dimensional map received from the server to perform a process foridentifying the self-position of the client device or a process fordisplaying map information to a user or the like who operates the clientdevice.

The following describes an example of the operation in this case. First,the server encodes the geometry information of the three-dimensional mapusing an octree structure or the like. Then, the sever layer-encodes theattribute information of the three-dimensional map using N LoDsestablished based on the geometry information. The server stores abitstream of the three-dimensional map obtained by the layer-encoding.

Next, in response to a send request for the map information from theclient device in the area managed by the server, the server sends thebitstream of the encoded three-dimensional map to the client device.

The client device receives the bitstream of the three-dimensional mapsent from the server, and decodes the geometry information and theattribute information of the three-dimensional map in accordance withthe intended use of the client device. For example, when the clientdevice performs highly accurate estimation of the self-position usingthe geometry information and the attribute information in N LoDs, theclient device determines that a decoding result to the densethree-dimensional points is necessary as the attribute information, anddecodes all the information in the bitstream.

Moreover, when the client device displays the three-dimensional mapinformation to a user or the like, the client device determines that adecoding result to the sparse three-dimensional points is necessary asthe attribute information, and decodes the geometry information and theattribute information in M LoDs (M<N) starting from an upper layer LoD0.

In this way, the processing load of the client device can be reduced bychanging LoDs for the attribute information to be decoded in accordancewith the intended use of the client device.

In the example shown in FIG. 51 , for example, the three-dimensional mapincludes geometry information and attribute information. The geometryinformation is encoded using the octree. The attribute information isencoded using N LoDs.

Client device A performs highly accurate estimation of theself-position. In this case, client device A determines that all thegeometry information and all the attribute information are necessary,and decodes all the geometry information and all the attributeinformation constructed from N LoDs in the bitstream.

Client device B displays the three-dimensional map to a user. In thiscase, client device B determines that the geometry information and theattribute information in M LoDs (M<N) are necessary, and decodes thegeometry information and the attribute information constructed from MLoDs in the bitstream.

It is to be noted that the server may broadcast the three-dimensionalmap to the client devices, or multicast or unicast the three-dimensionalmap to the client devices.

The following describes a variation of the system according to thepresent embodiment. In the three-dimensional data encoding device, whenattribute information of a current three-dimensional point to be encodedis layer-encoded using LoDs, the three-dimensional data encoding devicemay encode the attribute information in layers down to LoD required bythe three-dimensional data decoding device and need not encode theattribute information in layers not required. For example, when thetotal number of LoDs is N, the three-dimensional data encoding devicemay generate a bitstream by encoding M LoDs (M<N), i.e., layers from theuppermost layer LoD0 to LoD(M-1), and not encoding the remaining LoDs,i.e., layers down to LoD(N-1). With this, in response to a request fromthe three-dimensional data decoding device, the three-dimensional dataencoding device can provide a bitstream in which the attributeinformation from LoD0 to LoD(M-1) required by the three-dimensional datadecoding device is encoded.

FIG. 52 is a diagram illustrating the foregoing use case. In the exampleshown in FIG. 52 , a server stores a three-dimensional map obtained byencoding three-dimensional geometry information and attributeinformation. The server (the three-dimensional data encoding device)unicasts, in response to a request from the client device, thethree-dimensional map to a client device (the three-dimensional datadecoding device: for example, a vehicle, a drone, etc.) in an areamanaged by the server, and the client device uses the three-dimensionalmap received from the server to perform a process for identifying theself-position of the client device or a process for displaying mapinformation to a user or the like who operates the client device.

The following describes an example of the operation in this case. First,the server encodes the geometry information of the three-dimensional mapusing an octree structure, or the like. Then, the sever generates abitstream of three-dimensional map A by layer-encoding the attributeinformation of the three-dimensional map using N LoDs established basedon the geometry information, and stores the generated bitstream in theserver. The sever also generates a bitstream of three-dimensional map Bby layer-encoding the attribute information of the three-dimensional mapusing M LoDs (M<N) established based on the geometry information, andstores the generated bitstream in the server.

Next, the client device requests the server to send thethree-dimensional map in accordance with the intended use of the clientdevice. For example, when the client device performs highly accurateestimation of the self-position using the geometry information and theattribute information in N LoDs, the client device determines that adecoding result to the dense three-dimensional points is necessary asthe attribute information, and requests the server to send the bitstreamof three-dimensional map A. Moreover, when the client device displaysthe three-dimensional map information to a user or the like, the clientdevice determines that a decoding result to the sparse three-dimensionalpoints is necessary as the attribute information, and requests theserver to send the bitstream of three-dimensional map B including thegeometry information and the attribute information in M LoDs (M<N)starting from an upper layer LoD0. Then, in response to the send requestfor the map information from the client device, the server sends thebitstream of encoded three-dimensional map A or B to the client device.

The client device receives the bitstream of three-dimensional map A or Bsent from the server in accordance with the intended use of the clientdevice, and decodes the received bitstream. In this way, the serverchanges a bitstream to be sent, in accordance with the intended use ofthe client device. With this, it is possible to reduce the processingload of the client device.

In the example shown in FIG. 52 , the server stores three-dimensionalmap A and three-dimensional map B. The server generatesthree-dimensional map A by encoding the geometry information of thethree-dimensional map using, for example, an octree structure, andencoding the attribute information of the three-dimensional map using NLoDs. In other words, NumLoD included in the bitstream ofthree-dimensional map A indicates N.

The server also generates three-dimensional map B by encoding thegeometry information of the three-dimensional map using, for example, anoctree structure, and encoding the attribute information of thethree-dimensional map using M LoDs. In other words, NumLoD included inthe bitstream of three-dimensional map B indicates M.

Client device A performs highly accurate estimation of theself-position. In this case, client device A determines that all thegeometry information and all the attribute information are necessary,and requests the server to send three-dimensional map A including allthe geometry information and the attribute information constructed fromN LoDs. Client device A receives three-dimensional map A, and decodesall the geometry information and the attribute information constructedfrom N LoDs.

Client device B displays the three-dimensional map to a user. In thiscase, client device B determines that all the geometry information andthe attribute information in M LoDs (M<N) are necessary, and requeststhe server to send three-dimensional map B including all the geometryinformation and the attribute information constructed from M LoDs.Client device B receives three-dimensional map B, and decodes all thegeometry information and the attribute information constructed from MLoDs.

It is to be noted that in addition to three-dimensional map B, theserver (the three-dimensional data encoding device) may generatethree-dimensional map C in which attribute information in the remainingN-M LoDs is encoded, and send three-dimensional map C to client device Bin response to the request from client device B. Moreover, client deviceB may obtain the decoding result of N LoDs using the bitstreams ofthree-dimensional maps B and C.

Hereinafter, an example of an application process will be described.FIG. 53 is a flowchart illustrating an example of the applicationprocess. When an application operation is started, a three-dimensionaldata demultiplexing device obtains an ISOBMFF file including point clouddata and a plurality of pieces of encoded data (S7301). For example, thethree-dimensional data demultiplexing device may obtain the ISOBMFF filethrough communication, or may read the ISOBMFF file from the accumulateddata.

Next, the three-dimensional data demultiplexing device analyzes thegeneral configuration information in the ISOBMFF file, and specifies thedata to be used for the application (S7302). For example, thethree-dimensional data demultiplexing device obtains data that is usedfor processing, and does not obtain data that is not used forprocessing.

Next, the three-dimensional data demultiplexing device extracts one ormore pieces of data to be used for the application, and analyzes theconfiguration information on the data (S7303).

When the type of the data is encoded data (encoded data in S7304), thethree-dimensional data demultiplexing device converts the ISOBMFF to anencoded stream, and extracts a timestamp (S7305). Additionally, thethree-dimensional data demultiplexing device refers to, for example, theflag indicating whether or not the synchronization between data isaligned to determine whether or not the synchronization between data isaligned, and may perform a synchronization process when not aligned.

Next, the three-dimensional data demultiplexing device decodes the datawith a predetermined method according to the timestamp and the otherinstructions, and processes the decoded data (S7306).

On the other hand, when the type of the data is RAW data (RAW data inS7304), the three-dimensional data demultiplexing device extracts thedata and timestamp (S7307). Additionally, the three-dimensional datademultiplexing device may refer to, for example, the flag indicatingwhether or not the synchronization between data is aligned to determinewhether or not the synchronization between data is aligned, and mayperform a synchronization process when not aligned. Next, thethree-dimensional data demultiplexing device processes the dataaccording to the timestamp and the other instructions (S7308).

For example, an example will be described in which the sensor signalsobtained by a beam LiDAR, a FLASH LiDAR, and a camera are encoded andmultiplexed with respective different encoding schemes. FIG. 54 is adiagram illustrating examples of the sensor ranges of a beam LiDAR, aFLASH LiDAR, and a camera. For example, the beam LiDAR detects alldirections in the periphery of a vehicle (sensor), and the FLASH LiDARand the camera detect the range in one direction (for example, thefront) of the vehicle.

In the case of an application that integrally handles a LiDAR pointcloud, the three-dimensional data demultiplexing device refers to thegeneral configuration information, and extracts and decodes the encodeddata of the beam LiDAR and the FLASH LiDAR. Additionally, thethree-dimensional data demultiplexing device does not extract cameraimages.

According to the timestamps of the beam LiDAR and the FLASH LiDAR, thethree-dimensional data demultiplexing device simultaneously processesthe respective encoded data of the time of the same timestamp.

For example, the three-dimensional data demultiplexing device maypresent the processed data with a presentation device, may synthesizethe point cloud data of the beam LiDAR and the FLASH LiDAR, or mayperform a process such as rendering.

Additionally, in the case of an application that performs calibrationbetween data, the three-dimensional data demultiplexing device mayextract sensor geometry information, and use the sensor geometryinformation in the application.

For example, the three-dimensional data demultiplexing device may selectwhether to use beam LiDAR information or FLASH LiDAR information in theapplication, and may switch the process according to the selectionresult.

In this manner, since it is possible to adaptively change the obtainingof data and the encoding process according to the process of theapplication, the processing amount and the power consumption can bereduced.

Hereinafter, a use case in automated driving will be described. FIG. 55is a diagram illustrating a configuration example of an automateddriving system. This automated driving system includes cloud server7350, and edge 7360 such as an in-vehicle device or a mobile device.Cloud server 7350 includes demultiplexer 7351, decoders 7352A, 7352B,and 7355, point cloud data synthesizer 7353, large data accumulator7354, comparator 7356, and encoder 7357. Edge 7360 includes sensors7361A and 7361B, point cloud data generators 7362A and 7362B,synchronizer 7363, encoders 7364A and 7364B, multiplexer 7365, updatedata accumulator 7366, demultiplexer 7367, decoder 7368, filter 7369,self-position estimator 7370, and driving controller 7371.

In this system, edge 7360 downloads large data, which is largepoint-cloud map data accumulated in cloud server 7350. Edge 7360performs a self-position estimation process of edge 7360 (a vehicle or aterminal) by matching the large data with the sensor informationobtained by edge 7360. Additionally, edge 7360 uploads the obtainedsensor information to cloud server 7350, and updates the large data tothe latest map data.

Additionally, in various applications that handle point cloud data inthe system, point cloud data with different encoding methods arehandled.

Cloud server 7350 encodes and multiplexes large data. Specifically,encoder 7357 performs encoding by using a third encoding method suitablefor encoding a large point cloud. Additionally, encoder 7357 multiplexesencoded data. Large data accumulator 7354 accumulates the data encodedand multiplexed by encoder 7357.

Edge 7360 performs sensing. Specifically, point cloud data generator7362A generates first point cloud data (geometry information (geometry)and attribute information) by using the sensing information obtained bysensor 7361A. Point cloud data generator 7362B generates second pointcloud data (geometry information and attribute information) by using thesensing information obtained by sensor 7361B. The generated first pointcloud data and second point cloud data are used for the self-positionestimation or vehicle control of automated driving, or for map updating.In each process, a part of information of the first point cloud data andthe second point cloud data may be used.

Edge 7360 performs the self-position estimation. Specifically, edge 7360downloads large data from cloud server 7350. Demultiplexer 7367 obtainsencoded data by demultiplexing the large data in a file format. Decoder7368 obtains large data, which is large point-cloud map data, bydecoding the obtained encoded data.

Self-position estimator 7370 estimates the self-position in the map of avehicle by matching the obtained large data with the first point clouddata and the second point cloud data generated by point cloud datagenerators 7362A and 7362B. Additionally, driving controller 7371 usesthe matching result or the self-position estimation result for drivingcontrol.

Note that self-position estimator 7370 and driving controller 7371 mayextract specific information, such as geometry information, of the largedata, and may perform processes by using the extracted information.Additionally, filter 7369 performs a process such as correction ordecimation on the first point cloud data and the second point clouddata. Self-position estimator 7370 and driving controller 7371 may usethe first point cloud data and second point cloud data on which theprocess has been performed. Additionally, self-position estimator 7370and driving controller 7371 may use the sensor signals obtained bysensors 7361A and 7361B.

Synchronizer 7363 performs time synchronization and geometry correctionbetween a plurality of sensor signals or the pieces of data of aplurality of pieces of point cloud data. Additionally, synchronizer 7363may correct the geometry information on the sensor signal or point clouddata to match the large data, based on geometry correction informationon the large data and sensor data generated by the self-positionestimation process.

Note that synchronization and geometry correction may be performed notby edge 7360, but by cloud server 7350. In this case, edge 7360 maymultiplex the synchronization information and the geometry informationto transmit the synchronization information and the geometry informationto cloud server 7350.

Edge 7360 encodes and multiplexes the sensor signal or point cloud data.Specifically, the sensor signal or point cloud data is encoded by usinga first encoding method or a second encoding method suitable forencoding each signal. For example, encoder 7364A generates first encodeddata by encoding first point cloud data by using the first encodingmethod. Encoder 7364B generates second encoded data by encoding secondpoint cloud data by using the second encoding method.

Multiplexer 7365 generates a multiplexed signal by multiplexing thefirst encoded data, the second encoded data, the synchronizationinformation, and the like. Update data accumulator 7366 accumulates thegenerated multiplexed signal. Additionally, update data accumulator 7366uploads the multiplexed signal to cloud server 7350.

Cloud server 7350 synthesizes the point cloud data. Specifically,demultiplexer 7351 obtains the first encoded data and the second encodeddata by demultiplexing the multiplexed signal uploaded to cloud server7350. Decoder 7352A obtains the first point cloud data (or sensorsignal) by decoding the first encoded data. Decoder 7352B obtains thesecond point cloud data (or sensor signal) by decoding the secondencoded data.

Point cloud data synthesizer 7353 synthesizes the first point cloud dataand the second point cloud data with a predetermined method. When thesynchronization information and the geometry correction information aremultiplexed in the multiplexed signal, point cloud data synthesizer 7353may perform synthesis by using these pieces of information.

Decoder 7355 demultiplexes and decodes the large data accumulated inlarge data accumulator 7354. Comparator 7356 compares the point clouddata generated based on the sensor signal obtained by edge 7360 with thelarge data held by cloud server 7350, and determines the point clouddata that needs to be updated. Comparator 7356 updates the point clouddata that is determined to need to be updated of the large data to thepoint cloud data obtained from edge 7360.

Encoder 7357 encodes and multiplexes the updated large data, andaccumulates the obtained data in large data accumulator 7354.

As described above, the signals to be handled may be different, and thesignals to be multiplexed or encoding methods may be different,according to the usage or applications to be used. Even in such a case,flexible decoding and application processes are enabled by multiplexingdata of various encoding schemes by using the present embodiment.Additionally, even in a case where the encoding schemes of signals aredifferent, by conversion to an encoding scheme suitable fordemultiplexing, decoding, data conversion, encoding, and multiplexingprocessing, it becomes possible to build various applications andsystems, and to offer of flexible services.

Hereinafter, an example of decoding and application of divided data willbe described. First, the information on divided data will be described.FIG. 56 is a diagram illustrating a configuration example of abitstream. The general information of divided data indicates, for eachdivided data, the sensor ID (sensor_id) and data ID (data_id) of thedivided data. Note that the data ID is also indicated in the header ofeach encoded data.

Note that, as in FIG. 41 , the general information of divided dataillustrated in FIG. 56 includes, in addition to the sensor ID, at leastone of the sensor information (Sensor), the version (Version) of thesensor, the maker name (Maker) of the sensor, the mount information(Mount Info.) of the sensor, and the position coordinates of the sensor(World Coordinate). Accordingly, the three-dimensional data decodingdevice can obtain the information on various sensors from theconfiguration information.

The general information of divided data may be stored in SPS, GPS, orAPS, which is the metadata, or may be stored in SEI, which is themetadata not required for encoding. Additionally, at the time ofmultiplexing, the three-dimensional data encoding device stores the SEIin a file of ISOBMFF. The three-dimensional data decoding device canobtain desired divided data based on the metadata.

In FIG. 56 , SPS is the metadata of the entire encoded data, GPS is themetadata of the geometry information, APS is the metadata for eachattribute information, G is encoded data of the geometry information foreach divided data, and A1, etc. are encoded data of the attributeinformation for each divided data.

Next, an application example of divided data will be described. Anexample of application will be described in which an arbitrary pointcloud is selected, and the selected point cloud is presented. FIG. 57 isa flowchart of a point cloud selection process performed by thisapplication. FIG. 58 to FIG. 60 are diagrams illustrating screenexamples of the point cloud selection process.

As illustrated in FIG. 58 , the three-dimensional data decoding devicethat performs the application includes, for example, a UI unit thatdisplays an input UI (user interface) 8661 for selecting an arbitrarypoint cloud. Input UI 8661 includes presenter 8662 that presents theselected point cloud, and an operation unit (buttons 8663 and 8664) thatreceives operations by a user. After a point cloud is selected in UI8661, the three-dimensional data decoding device obtains desired datafrom accumulator 8665.

First, based on an operation by the user on input UI 8661, the pointcloud information that the user wants to display is selected (S8631).Specifically, by selecting button 8663, the point cloud based on sensor1 is selected. By selecting button 8664, the point cloud based on sensor2 is selected. Alternatively, by selecting both button 8663 and button8664, the point cloud based on sensor 1 and the point cloud based onsensor 2 are selected. Note that it is an example of the selectionmethod of point cloud, and it is not limited to this.

Next, the three-dimensional data decoding device analyzes the generalinformation of divided data included in the multiplexed signal(bitstream) or encoded data, and specifies the data ID (data_id) of thedivided data constituting the selected point cloud from the sensor ID(sensor_id) of the selected sensor (58632). Next, the three-dimensionaldata decoding device extracts, from the multiplexed signal, the encodeddata including the specified and desired data ID, and decodes theextracted encoded data to decode the point cloud based on the selectedsensor (58633). Note that the three-dimensional data decoding devicedoes not decode the other encoded data.

Lastly, the three-dimensional data decoding device presents (forexample, displays) the decoded point cloud (S8634). FIG. 59 illustratesan example in the case where button 8663 for sensor 1 is pressed, andthe point cloud of sensor 1 is presented. FIG. 60 illustrates an examplein the case where both button 8663 for sensor 1 and button 8664 forsensor 2 are pressed, and the point clouds of sensor 1 and sensor 2 arepresented.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

Although only some exemplary embodiments of the present disclosure havebeen described in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data decoding methodcomprising: performing motion compensation on a second point cloud byusing motion from a decoded first point cloud to the second point cloud,to generate a third point cloud having a position which is a position ofthe second point cloud that is shifted based on the motion; merging thefirst point cloud and the third point cloud, to generate a referencepoint cloud; decoding an N-ary tree structure of a current point cloudusing the reference point cloud, where N is an integer greater than orequal to 2; and generating a decoded point cloud of the current pointcloud from the N-ary tree structure of the current point cloud.
 2. Thethree-dimensional data decoding method according to claim 1, wherein thedecoding of the N-ary tree structure of the current point cloudincludes: performing motion compensation for the current point cloud onthe reference point cloud; generating an N-ary tree structure of thereference point cloud that has been motion compensated; and decoding theN-ary tree structure of the current point cloud using the N-ary treestructure of the reference point cloud.
 3. The three-dimensional datadecoding method according to claim 1, further comprising: performingmotion compensation for the reference point cloud on the decoded pointcloud of the current point cloud; and merging the decoded point cloudthat has been motion compensated with the reference point cloud toupdate the reference point cloud.
 4. The three-dimensional data decodingmethod according to claim 1, wherein each of the plurality of decodedpoint clouds belongs to a different frame than the current point cloud.5. The three-dimensional data decoding method according to claim 1,wherein each of the plurality of decoded point clouds belongs to a sameframe as the current point cloud.
 6. The three-dimensional data decodingmethod according to claim 1, further comprising: obtaining, from controlinformation which is common to a plurality of point clouds, firstinformation indicating whether execution of decoding using the referencepoint cloud is permitted.
 7. A three-dimensional data decoding methodcomprising: performing motion compensation on a plurality of decodedpoint clouds; merging the plurality of decoded point clouds that havebeen motion compensated, to generate a reference point cloud; decodingan N-ary tree structure of a current point cloud using the referencepoint cloud, where N is an integer greater than or equal to 2; andgenerating a decoded point cloud of the current point cloud from theN-ary tree structure of the current point cloud; wherein the decoding ofthe N-ary tree structure of the current point cloud includes: entropydecoding the N-ary tree structure of the current point cloud; andcontrolling a probability parameter to be used in the entropy decoding,based on the reference point cloud.
 8. A three-dimensional data decodingmethod comprising: performing motion compensation on a plurality ofdecoded point clouds; merging the plurality of decoded point clouds thathave been motion compensated, to generate a reference point cloud;decoding an N-ary tree structure of a current point cloud using thereference point cloud, where N is an integer greater than or equal to 2;generating a decoded point cloud of the current point cloud from theN-ary tree structure of the current point cloud; obtaining, from controlinformation which is common to a plurality of point clouds, firstinformation indicating whether execution of decoding using the referencepoint cloud is permitted; and obtaining, from the control information,second information on a total number of the plurality of decoded pointclouds, when the first information indicates that the execution of thedecoding using the reference point cloud is permitted.
 9. Athree-dimensional data encoding device comprising: a processor; andmemory, wherein using the memory, the processor: performs motioncompensation on a second point cloud by using motion from a decodedfirst point cloud to the second point cloud, to generate a third pointcloud having a position which is a position of the second point cloudthat is shifted based on the motion; merges the first point cloud andthe third point cloud, to generate a reference point cloud; generates anN-ary tree structure of a current point cloud, where N is an integergreater than or equal to 2; and encodes the N-ary tree structure of thecurrent point cloud using the reference point cloud.
 10. Athree-dimensional data decoding device comprising: a processor; andmemory, wherein using the memory, the processor: performs motioncompensation on a second point cloud by using motion from a decodedfirst point cloud to the second point cloud, to generate a third pointcloud having a position which is a position of the second point cloudthat is shifted based on the motion; merges the first point cloud andthe third point cloud, to generate a reference point cloud; decodes anN-ary tree structure of a current point cloud using the reference pointcloud, where N is an integer greater than or equal to 2; and generates adecoded point cloud of the current point cloud from the N-ary treestructure of the current point cloud.
 11. A three-dimensional datadecoding device comprising: a processor; and memory, wherein using thememory, the processor: performs motion compensation on a plurality ofdecoded point clouds; merges the plurality of decoded point clouds thathave been motion compensated, to generate a reference point cloud;performs entropy decoding of an N-ary tree structure of a current pointcloud using the reference point cloud, and controls a probabilityparameter to be used in the entropy decoding, based on the referencepoint cloud, where N is an integer greater than or equal to 2; andgenerates a decoded point cloud of the current point cloud from theN-ary tree structure of the current point cloud.